Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study
Open accessAbstract
BACKGROUND: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. METHODS: In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). FINDINGS: Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001). INTERPRETATION: RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer. FUNDING: National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Integration of MRI radiomics and WSI enhances NAC response prediction in patients with locally advanced rectal cancer.
“In recent studies, researchers confirmed that the integration of MRI radiomics and WSI enhances NAC response prediction in patients with locally advanced rectal cancer (17, 18).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [18]
The model was developed based on single-center data.
“the model was developed based on single-center data.”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [18]
Cites
Computational Radiomics System to Decode the Radiographic Phenotype
(2017) · doi:10.1158/0008-5472.can-17-0339 · Cited by 6,593 · see citing works
Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome
Galon, J., Costes, A., Sanchez-Cabo, F., Kirilovsky, A., Mlecnik, B., Lagorce-Pagès, C., Tosolini, M., Camus, M., Berger, A., Wind, P., et al. (2006) · Science · doi:10.1126/science.1129139 · Cited by 6,516 · see citing works
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,077 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Maas, M., Nelemans, P. J., Valentini, V., Das, P., Rödel, C. M., Kuo, L., Calvo, F. Á., García‐Aguilar, J., Glynne‐Jones, R., Haustermans, K. M., et al. (2010) · The Lancet Oncology · doi:10.1016/s1470-2045(10)70172-8 · Cited by 1,945 · see citing works
Huang, Y., Liang, C., He, L., Tian, J., Liang, C., Chen, X., Ma, Z., & Liu, Z. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.65.9128 · Cited by 1,745 · see citing works
Self-citationKather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082 · see citing works
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972 · see citing works
Renehan, A. G., Malcomson, L., Emsley, R., et al. (2015) · The Lancet Oncology · doi:10.1016/s1470-2045(15)00467-2 · Cited by 802 · see citing works
Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works
Reporting of artificial intelligence prediction models
Collins, G. S., & Moons, K. G. M. (2019) · The Lancet · doi:10.1016/s0140-6736(19)30037-6 · Cited by 701 · see citing works
Deep learning for prediction of colorectal cancer outcome: a discovery and validation study
Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32998-8 · Cited by 627 · see citing works
Fokas, E., Allgäuer, M., Polat, B., Klautke, G., Grabenbauer, G. G., Fietkau, R., Kuhnt, T., Staib, L., Brunner, T. B., Grosu, A., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.19.00308 · Cited by 550 · see citing works
Liu, Z., Zhang, X.-Y., Shi, Y., Wang, L., Zhu, H., Tang, Z., Wang, S., Li, X.-T., Tian, J., & Sun, Y. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-1038 · Cited by 548 · see citing works
Self-citationTransparency and reproducibility in artificial intelligence
Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature · doi:10.1038/s41586-020-2766-y · Cited by 540 · see citing works
Pantanowitz, L., Quiroga‐Garza, G. M., Bien, L., Heled, R., Laifenfeld, D., Linhart, C., Sandbank, J., Shach, A. A., Shalev, V., Vecsler, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30159-x · Cited by 361 · see citing works
Ghadimi, Β. M., Grade, M., Difilippantonio, M. J., Varma, S., Simon, R. M., Montagna, C., FÜZESI, L., Langer, C., Heinz Becker, A., Liersch, T., et al. (2005) · Journal of Clinical Oncology · doi:10.1200/jco.2005.00.406 · Cited by 345 · see citing works
Daydé, D., Tanaka, I., Jain, R., Tai, M.-C., & Taguchi, A. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18030573 · Cited by 181 · see citing works
Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer
Liu, Z., Meng, X., Zhang, H., Li, Z., Liu, J., Sun, K., Meng, Y., Dai, W., Xie, P., Ding, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18162-9 · Cited by 171 · see citing works
Self-citationHuang, P., Lin, C. T., Li, Y., Tammemägi, M. C., Brock, M. V., Atkar-Khattra, S., Xu, Y., Hu, P., Mayo, J. R., Schmidt, H., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30159-1 · Cited by 151 · see citing works
Ueno, H., Ishiguro, M., Nakatani, E., Ishikawa, T., Uetake, H., Matsuda, C., Nakamoto, Y., Kotake, M., Kurachi, K., Egawa, T., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.02059 · Cited by 149 · see citing works
Xiao, W., Huang, X., Wang, J. H., Lin, D. R., Zhu, Y., Chen, C., Yang, Y., Xiao, J., Zhao, L., Li, J.-P. O., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30288-0 · Cited by 118 · see citing works
Shao, L., Liu, Z., Feng, L., Lou, X., Li, Z., Zhang, X.-Y., Wan, X., Zhou, X., Sun, K., Zhang, D.-F., et al. (2020) · Annals of Surgical Oncology · doi:10.1245/s10434-020-08659-4 · Cited by 92 · see citing works
Self-citationBulens, P., Couwenberg, A. M., Intven, M., Debucquoy, A., Vandecaveye, V., Cutsem, E. V., D’Hoore, A., Wolthuis, A. M., Mukherjee, P., Gevaert, O., et al. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.07.033 · Cited by 91 · see citing works
Huh, J. W., Lee, W. Y., Shin, J. K., et al. (2019) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-018-2804-4 · Cited by 48 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works
Mao, N., Dai, Y., Zhou, H., Lin, F., Zheng, T., Li, Z., Yang, P., Zhao, F., Li, Q., Wang, W., et al. (2025) · Science Advances · doi:10.1126/sciadv.adr1576 · Cited by 51 · see citing works
“Second, with reference to the literature ( 13 , 16 , 18 ) and considering the limited amount of data in each external hospital, the model was developed based on single-center data.”
Self-citation
- Authors
- Feng, L., Liu, Z., Li, C., Li, Z., Lou, X., Shao, L., Wang, Y., Huang, Y., Chen, H., Pang, X., Liu, S., He, F., Zheng, J., Meng, X., Xie, P., Yang, G., Ding, Y., Wei, M., Yun, J., Hung, M.-C., Zhou, W., Wahl, D. R., Lan, P., Tian, J., & Wan, X.
- Affiliations
- Sixth Affiliated Hospital of Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- Sun Yat-sen University Cancer Center (CN)
- Kunming Medical University (CN)
- Southeast University (CN)
- Nanfang Hospital (CN)
- The University of Texas MD Anderson Cancer Center (US)
- University of Michigan (US)
- Corresponding author
- Tian, Jie
- ORCID
- Venue
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 4
- Issue
- 1
- Pages
- e8-e17
- ISSN
- 2589-7500 (ISSN-L)
- OpenAlex
- W4200111739
- Cited by
- 339
- References
- 29
- Citations by year
- 2026: 64 · 2025: 108 · 2024: 85 · 2023: 59 · 2022: 22
- FWCI
- 22.01
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Digital Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81922040, 92059103, 81872188, 81902867, 81903152, 82001986)
- Youth Innovation Promotion Association of the Chinese Academy of Sciences (2019136)
- Sun Yat-sen University Cancer Center
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › MRI in cancer diagnosis
- Keywords
- locally advanced rectal cancer, pathological complete response, neoadjuvant chemoradiotherapy, rapids, radiomics, pathomics, tumor microenvironment, whole slide images, pre-treatment MRI, treatment response prediction, artificial intelligence, diffusion-weighted imaging, T2-weighted imaging, T1-weighted imaging
- MeSH terms
- Aged, Artificial Intelligence, Female, Humans, Male, Middle Aged, Prospective Studies, Rectal Neoplasms, Retrospective Studies, Neoadjuvant Therapy
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.
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
