The DOI matches its Crossref record; 33 of the 36 references it cites were found in a registry or an index; 11 of the 22 sources its claims cite were checked for support.
Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis
Open accessAbstract
Prediagnostic CT radiomics-based ML models for PDAC detection, compared against radiologists.
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Claims the paper makes
21 Own result · 16 Own method · 5 Partly supported · 5 No full text of the sources · 5 Own interpretation · 3 Supported · 1 Supported so far · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In patients with PDAC, subtle indirect findings suggestive of cancer (eg, pancreatic duct cutoff or dilatation, focal pancreatic atrophy) can be present on CTs at the prediagnostic stage (ie, 3–36 months before clinical diagnosis).2,7,8”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Partly supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology sourceExperimental — accuracy not yet measured
The Introduction describes CT scans obtained 3–36 months before PDAC diagnosis, and the Results identify pancreatic duct cutoff or dilatation as a prediagnostic finding. The paper’s listed CT findings do not include focal pancreatic atrophy, so it does not support that example in the citing sentence. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [7] Gangi, 2004, Time interval between abnormalities seen on CT and the clinical diagnosis of pancreatic cancer: retrospective review of CT scans obtained before diagnosis, Am J Roentgenol source
- Not checked — no full text of the sourcecites [8] Pelaez-Luna, 2007, Resectability of presymptomatic pancreatic cancer and its relationship to onset of diabetes: a retrospective review of CT scans and fasting glucose values prior to diagnosis, Am J Gastroenterol source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Second, early detection before the decline in performance status from cancer-induced cachexia increases the prospect of surgical resection in eligible patients.2,5”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology source
The Discussion states that tumors detected earlier are more likely to be resectable and that early detection identifies patients before a significant decline in performance status. It also describes poor performance status as a hallmark of late-stage cachexia, supporting the citing sentence's paraphrase. (LLM, conf=0.89)
- Partly supportedcites [5] Hart, 2019, Is screening for pancreatic cancer in high-risk individuals one step closer or a fool's errand?, Clin Gastroenterol Hepatol sourceExperimental — accuracy not yet measured
The paper’s opening section (page unavailable) says that most PDAC is diagnosed at an advanced stage, only 10–15% of patients are candidates for potentially curative treatment including surgery, and early diagnosis requires detection before symptoms. It does not mention cachexia or declining performance status as the reason earlier detection could improve the prospect of resection. (LLM, conf=0.84)
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“One such cohort is subjects with new-onset diabetes and a high score (≥3) on the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) model.4”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Partly supportedcites [4] Yuan, 2020, Diabetes, weight change, and pancreatic cancer risk, JAMA Oncol sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) identifies recent-onset diabetes accompanied by weight loss as a higher-risk group and says whether that group should undergo pancreatic cancer surveillance warrants prospective study. The paper does not identify an END-PAC score of ≥3 as the criterion for that group or assess screening for people meeting that cutoff. (LLM, conf=0.86)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“A computed tomography (CT) scan performed at the onset of new-onset diabetes in such a high-risk group has the potential to increase the proportion of resectable PDACs to 3 times higher than the current resectability rate.2”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology source
The Discussion states that CT at the onset of new-onset diabetes is predicted to make the proportion of resectable tumors three times higher than the current rate. It identifies this as a prediction, not a prospectively demonstrated outcome, consistent with the citing sentence’s 'has the potential' wording. (LLM, conf=0.95)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Second, these findings are not specific for early PDAC because they are also seen in control subjects.2,11”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Partly supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology sourceExperimental — accuracy not yet measured
The Results section ('Parameters assessed on CT'; page not available) compares pre-diagnostic PDAC scans with matched controls and says the controls had 'negligible findings.' This allows for some findings in controls, but does not support the unqualified suggestion that these signs are generally nonspecific for early PDAC; they were much more frequent in cases. (LLM, conf=0.80)
- Not checked — no full text of the sourcecites [11] Toshima, 2021, CT abnormalities of the pancreas associated with the subsequent diagnosis of clinical stage I pancreatic ductal adenocarcinoma more than 1 year later: a case-control study, Am J Roentgenol source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“This is a major challenge because the cancer can rapidly advance from being subclinical on imaging to stage IV.2,11”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology source
The Results passage reports that CT abnormalities became increasingly common in the months before PDAC diagnosis, while distant metastases—the paper’s CT stage IV—were seen only at diagnosis. The Discussion’s CT timeline places earlier, pancreas-confined findings roughly 9–13 months before diagnosis, supporting the claim that disease may advance from imaging-invisible to metastatic within that period. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [11] Toshima, 2021, CT abnormalities of the pancreas associated with the subsequent diagnosis of clinical stage I pancreatic ductal adenocarcinoma more than 1 year later: a case-control study, Am J Roentgenol source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97–1092) days.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > RESULTS:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“median interval between CT and PDAC diagnosis: 398 days”
- Also stated in: § Study Participants · plain
“The median (range) time interval between prediagnostic CTs and histopathological diagnosis of PDAC was 398 (93–1092) days.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > METHODS:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Study Participants · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there was no statistically significant difference in accuracy of SVM model when the cases in the test subset were stratified by different vendors (Siemens, Toshiba, GE, and Philips were 92.7%, 92.1%, 88.2%, and 100%, respectively; P = .30).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Radiomics-based ML Models
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > RESULTS:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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- Probably not a substantial claim. 19
- It may state two separate claims as one. 17
Every source the paper cites for this claim was checked and supports it.
In the paper
“A large prospective trial, the Early Detection Initiative (EDI) (NCT04662879), sponsored by the Pancreatic Cancer Action Network, is under way to evaluate outcomes of a screening strategy in 12,500 participants by using the END-PAC model and CT.6”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Supportedcites [6] Chari, 2021, Early detection initiative: a randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma, Contemp Clin Trials source
The Abstract describes EDI as a randomized trial with a planned sample of 12,500, using ENDPAC risk stratification and CT imaging. The Organizational Structure section identifies it as NCT04662879 and says it has independent funding from the Pancreatic Cancer Action Network. Page numbers are not provided in our copy. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“To reduce the potentially confounding effect of slice thickness on the ML models, we used a recently described process21 to identify and then account for potential dependence of the extracted features on slice thickness”
- Also stated in: § Exclusion of CT Slice Thickness Dependent Radiomics Features · plain
“Features with a high AUC (> 0.8) were deemed to be biased by CT slice thickness and were, therefore, removed.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Exclusion of CT Slice Thickness Dependent Radiomics Features
What the sources say
- Supportedcites [21] Lu, 2021, Uncontrolled confounders may lead to false or overvalued radiomics signature: a proof of concept using survival analysis in a multicenter cohort of kidney cancer, Front Oncol source
The Methods section (‘Exclusion of Slice Thickness Related Features’) describes dividing scans into thin- and thick-slice groups, measuring how well each feature distinguished those groups using a C-index, and excluding features with values above 0.8 as slice-thickness-biased. The claim calls the measure AUC rather than C-index, but accurately conveys the binary-group screening and exclusion rule. (LLM, conf=0.89)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Also stated in: § Exclusion of CT Slice Thickness Dependent Radiomics Features · plain
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Early detection offers survival benefit beyond lead time, as tumors are smaller in volume and more likely to be resectable when detected earlier.2-4”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Partly supportedcites [2] Singh, 2020, Computerized tomography scan in pre-diagnostic pancreatic ductal adenocarcinoma: stages of progression and potential benefits of early intervention: a retrospective study, Pancreatology sourceExperimental — accuracy not yet measured
The Discussion reports that earlier-detected tumors were smaller and more likely to be resectable, and says the study suggests a survival benefit beyond lead time. The Abstract Conclusion calls that benefit likely, while the Introduction says a definitive answer requires a prospective randomized trial. The claim presents the survival benefit as established rather than suggested. (LLM, conf=0.85)
- Not checked — no full text of the sourcecites [3] Vasen, 2016, Benefit of surveillance for pancreatic cancer in high-risk individuals: outcome of long-term prospective follow-up studies from three european expert centers, J Clin Oncol source
- Partly supportedcites [4] Yuan, 2020, Diabetes, weight change, and pancreatic cancer risk, JAMA Oncol sourceExperimental — accuracy not yet measured
The Introduction reports earlier-stage diagnosis and longer survival among patients undergoing surveillance, and the Discussion says early-stage pancreatic cancer can be cured with aggressive treatment. The paper also describes a window in which some tumors are surgically resectable. It does not establish that the survival benefit exceeds lead-time effects or attribute greater resectability to smaller tumor volume. Page numbers are unavailable in our copy. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 45 CTs from our institution, the model correctly classified 16 of 17 (94%) prediagnostic CTs and 27 of 28 (96%) control CTs, yielding an overall accuracy of 95%. Of the 83 CTs from external institutions, the model correctly classified 27 of 28 (96%) prediagnostic CTs and 48 of 55 (87%) control CTs, yielding an overall accuracy of 90.4%”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Radiomics-based ML Models
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It correctly classified 43 of 45 (95%) prediagnostic CTs and 75 of 83 (90.3%) control CTs, yielding an overall accuracy of 92.2%.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Radiomics-based ML Models
plainn = 83- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean classification AUC of the 2 readers was 0.66 (CI, 0.46–0.86), which was significantly lower than each of the 4 ML classifiers (AUCs: 0.95–0.98) (P < .001)”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Multireader Evaluation of the Test Subset
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, interreader radiologist agreement was only fair (Cohen’s kappa 0.3) and their mean AUC (0.66; 0.46–0.86) was lower than each of the 4 ML models (AUCs: 0.95–0.98) (P < .001).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > RESULTS:
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other 3 models (KNN, RF, and XGB) had comparable AUCs (0.95, 0.95, and 0.96, respectively) to SVM (0.98)”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Radiomics-based ML Models
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“of the 176 CTs of healthy subjects from the independent internal set, the SVM model correctly classified the pancreas as normal in 163 CTs, yielding a specificity of 93%. The specificity was also generalizable on the public NIH-PCT dataset (n = 80) where the SVM model correctly classified the pancreas as normal in 77 CTs, yielding a specificity of 96%.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Results > Radiomics-based ML Models
plainn = 176- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A 5-fold stratified cross-validation–based randomized search was applied to evaluate each model’s hyperparameters. The parameters that yielded the highest cross-validation AUC were selected for each classifier model.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Model Development and Testing
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“These control CTs were also curated by radiologists to confirm optimal image quality, portal venous phase, and normal pancreas morphology.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Each CT was rereviewed by 1 of 3 radiologists (R1, R2, R3 with 2–4 years of post-radiology residency experience) who confirmed optimal image quality (eg, lack of motion artifacts compromising visual interpretation), portal venous phase of enhancement, absence of pancreatitis, focal solid or cystic lesion, and biliary or pancreatic duct stent (Figure 2).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“From this cohort, we selected an age-matched set of control patients (n = 265) (140 men, 125 women; median [range] age: 67 [30–89] years) who were not diagnosed subsequently with PDAC during 3 years or more of follow-up (Figure 1).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plainn = 265- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, to externally validate its specificity, the classifier with the highest AUC on the internal test subset was tested on the National Institutes of Health-Pancreas CT (NIH-PCT) dataset.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Interpretation was performed under identical ambient conditions with calibrated DICOM–compliant monitors at a PACS workstation (Visage Imaging, San Diego, CA).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Multireader Evaluation of the Test Subset
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Feature selection was done with least absolute shrinkage and selection operator (LASSO)20 logistic regression, which is an L1-regularization–based method that penalizes the L1-norm of the feature weight coefficients. Therefore, it reduces the model’s complexity by eliminating some of the coefficients (ie, coefficients become zero) and the corresponding features to minimize overfitting and improve”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Feature Extraction and Reduction
What the sources say
- Not checked — no full text of the sourcecites [20] Tibshirani, 1997, The lasso method for variable selection in the Cox model, Stat Med source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“However, these findings are often overlooked in clinical practice, leading to delayed diagnosis.9,10”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
What the sources say
- Not checked — no full text of the sourcecites [9] Kang, 2021, The implications of missed or misinterpreted cases of pancreatic ductal adenocarcinoma on imaging: a multi-centered population-based study, Eur Radiol source
- Not checked — no full text of the sourcecites [10] Kang, 2021, Factors associated with missed and misinterpreted cases of pancreatic ductal adenocarcinoma, Eur Radiol source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The public dataset includes pancreas segmentation masks, which we reviewed to ensure the accuracy of segmentation boundaries.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All the incorrectly classified CTs were reviewed in consensus by R1, R2, and R3 to identify any potential causes for misclassification.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Model Development and Testing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For external validation of the model with highest accuracy, we stratified our test subset according to the institution where CTs had been done: 45 (35.2%) were from our institution (17 [37.8%] prediagnostic CTs and 28 [62.2%] control CTs), whereas 83 (64.8%) were from external institutions (28 [33.7%] prediagnostic CTs and 55 [66.3%] control CTs).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Abstract > CONCLUSIONS:
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Standard acquisition and reconstruction parameters as per our previously published protocols17 had been followed for all CTs performed at our institution.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Volumetric Pancreas Segmentation
What the sources say
- Not checked — no full text of the sourcecites [17] Panda, 2021, Borderline resectable and locally advanced pancreatic cancer: FDG PET/MRI and CT tumor metrics for assessment of pathologic response to neoadjuvant therapy and prediction of survival, Am J Roentgenol source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Its specificity was also evaluated on an independent internal validation set (n = 176) and on the public NIH-PCT dataset (n = 80) with normal pancreas.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Model Development and Testing
plainn = 176- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The preceding dataset of prediagnostic CTs (n = 155) and age-matched control CTs with normal pancreas (n = 265) was randomly divided into training-validation (n = 292) and test (n = 128) subsets using a 70% / 30% split for ML model development and testing.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plainn = 128- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As mentioned previously, external validation of the model with highest accuracy was performed by stratifying the test subset according to the institution (35.2% CTs from our institution and 64.8% CTs from external institutions).”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Model Development and Testing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“For additional independent validation of the specificity, we created another internal hold-out set of 176 CTs (68 men, 108 women; median [range] age: 45 [19–94] years) who had normal pancreas and did not develop PDAC during at least 3 years or more of follow-up.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Study Participants
plainn = 176- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Hyperparameter for LASSO was evaluated using stratified 5-fold cross-validation–based grid search method on the training set (n = 292). The parameter that provided the highest cross-validation AUC was selected.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Feature Extraction and Reduction
plainn = 292- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two radiologist readers (R4 with 9 years and R5 with 3 years of post-residency experience) who were blinded to the outcomes and the distribution of prediagnostic vs control CTs independently evaluated the pancreas in the test subset CTs.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Materials and Methods > Multireader Evaluation of the Test Subset
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, radiologist readers had low interreader agreement, sensitivity, and discrimination accuracy, which shows that novel AI-based approaches can detect PDAC at a subclinical stage when it is beyond the scope of the human interrogation.”
§ Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis > Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
In many PDAC patients, the pancreas appears morphologically normal on CT before diagnosis.
“Importantly, the pancreas tends to be morphologically normal on CT at prediagnostic stage in many patients.”
The paper relies on this without support.
No public dataset of prediagnostic CT scans exists.
“There is no public dataset of prediagnostic CTs.”
The paper relies on this without support.
The prediagnostic CT dataset used in this study is the largest reported in the literature.
“The prediagnostic CT dataset used in this study is the largest reported in the literature.”
The paper relies on this without support.
There are no public datasets of prediagnostic CTs, which is a major barrier for investigating CT for early PDAC detection.
“However, there are no public datasets of prediagnostic CTs, which is one major barrier for investigation of CT for early detection of PDAC.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Mukherjee and colleagues compared four ML classifiers on 34 radiomics features selected from pre-diagnostic contrast-enhanced CT scans for the early detection of pancreatic carcinoma and found that SVM outperformed RF, a KNN, and XGBoost in diagnostic accuracy [14].”
From Applicability of Radiomics for Differentiation of Pancreatic Adenocarcinoma from Healthy Tissue of Pancreas by Using Magnetic Resonance Imaging and Machine Learning (2025) · cites this paper as [14]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In our statistics, several studies focused on PC or PC precursor lesions diagnosis or prediction by AI-assisted CT. Their AUC, accuracy, sensitivity, specificity were 0.79-0.999, 77.66%-99.2%, 76.64%-100%, 85.59%-98.5%, respectively 113-122.”
From Artificial intelligence in pancreatic cancer (2022) · cites this paper as [121]
Citations
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“[121] 420 cases CE-CT KNN, SVM, RF, and XGBoost PDAC detection AUC (0.”
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Self-citation
Metadata
- Authors
- Mukherjee, S., Patra, A., Khasawneh, H., Korfiatis, P., Rajamohan, N., Suman, G., Majumder, S., Panda, A., Johnson, M. P., Larson, N. B., Wright, D. E., Kline, T. L., Fletcher, J. G., Chari, S. T., & Goenka, A. H.
- Affiliations
- Mayo Clinic (US)
- Tata Medical Center (IN)
- Corresponding author
- Goenka, Ajit H.
- ORCID
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 163
- Issue
- 5
- Pages
- 1435-1446.e3
- ISSN
- 0016-5085 (ISSN-L)
- PMC
- PMC12285712
- OpenAlex
- W4283780227
- Semantic Scholar
- 703522bc0ca697394391ed9dd20d76b2e43d6691
- Cited by
- 187
- References
- 36
- Citations by year
- 2026: 35 · 2025: 61 · 2024: 47 · 2023: 35 · 2022: 9
- FWCI
- 21.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Gastroenterology (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Defense
- National Cancer Institute
- Congressionally Directed Medical Research Programs (R01CA256969)
- Mayo Clinic
- Department of Radiology, Weill Cornell Medical College
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Keywords
- pancreatic ductal adenocarcinoma, radiomics, early cancer detection, computed tomography, machine learning classification, support vector machine, radiologist comparison, pancreas segmentation, radiomic feature selection, lead time, cancer screening
- MeSH terms
- Machine Learning, Humans, Pancreatic Neoplasms, Retrospective Studies, Tomography, X-Ray Computed, Case-Control Studies, Carcinoma, Pancreatic Ductal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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