Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients
Open accessAbstract
Introduction: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. Methods: We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. Results: Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. Conclusions: In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Prelaj et al. used SHAP and demonstrated that the most relevant features in their model are clinical biomarkers that have previously been shown to be important.
“For explainability, they used SHAP, which demonstrated that the most relevant features in their model are clinical biomarkers that have previously been shown to be important36.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
CatBoost: unbiased boosting with categorical featuresPreprint
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09516 · Cited by 3,041 · see citing works
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0304-0 · Cited by 1,967 · see citing works
Reck, M., Rodríguez–Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.00149 · Cited by 1,670 · see citing works
CatBoost: gradient boosting with categorical features supportPreprint
Dorogush, A. V., Ershov, V., & Gulin, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.11363 · Cited by 1,362 · see citing works
Gadgeel, S. M., Rodríguez‐Abreu, D., Speranza, G., Esteban, E., Felip, E., Dómine, M., Hui, R., Hochmair, M. J., Clingan, P. R., Powell, S. F., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.03136 · Cited by 1,118 · see citing works
Self-citationMezquita, L., Auclin, E., Ferrara, R., Charrier, M., Remon, J., Planchard, D., Ponce, S., Ares, L. P., Leroy, L., Audigier-Valette, C., et al. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2017.4771 · Cited by 913 · see citing works
Self-citationFerrara, R., Mezquita, L., Texier, M., Lahmar, J., Audigier-Valette, C., Tessonnier, L., Mazieres, J., Zalcman, G., Brosseau, S., Le Moulec, S., et al. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2018.3676 · Cited by 720 · see citing works
Self-citationBorghaei, H., Gettinger, S. N., Vokes, E. E., Chow, L. Q. M., Burgio, M. A., de Castro, J., Płużański, A., Arrieta, O. G., Frontera, O. A., Chiari, R., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.20.01605 · Cited by 657 · see citing works
Self-citationPaz-Ares, L. G., Vicente, D., Tafreshi, A., Robinson, A. G., Parra, H. J. S., Mazières, J., Hermes, B., Çiçin, İ., Medgyasszay, B., Rodríguez‐Cid, J. R., et al. (2020) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2020.06.015 · Cited by 630 · see citing works
Moncada‐Torres, A., van Maaren, M. C., Hendriks, M. P., Siesling, S., & Geleijnse, G. (2021) · Scientific Reports · doi:10.1038/s41598-021-86327-7 · Cited by 340 · see citing works
Planchard, D., Popat, S., Kerr, K. M., Novello, S., Smit, E. F., Finn, C. F., Mok, T. S. K., Reck, M., Schil, P. E. V., Hellmann, M. D., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdy474 · Cited by 323 · see citing works
Pai-Scherf, L. H., Blumenthal, G. M., Li, H., Subramaniam, S., Mishra-Kalyani, P. S., He, K., Zhao, H., Yu, J., Paciga, M., Goldberg, K. B., et al. (2017) · The Oncologist · doi:10.1634/theoncologist.2017-0078 · Cited by 294 · see citing works
Predictive biomarkers of response for immune checkpoint inhibitors in non–small-cell lung cancer
Prelaj, A., Tay, R. Y., Ferrara, R., Chaput, N., Besse, B., & Califano, R. (2018) · European Journal of Cancer · doi:10.1016/j.ejca.2018.11.002 · Cited by 193 · see citing works
Self-citationJohannet, P. M., Coudray, N., Donnelly, D. M., Jour, G., Illa-Bochaca, I., Xia, Y., Johnson, D. B., Wheless, L., Patrinely, J. R., Nomikou, S., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2415 · Cited by 181 · see citing works
Pawar, U., O’Shea, D., Rea, S., & O’Reilly, R. (2020) · International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) · doi:10.1109/cybersa49311.2020.9139655 · Cited by 175 · see citing works
Hao, J., Kim, Y. S., Kim, T.-K., & Kang, M. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2500-z · Cited by 139 · see citing works
Triberti, S., Durosini, I., & Pravettoni, G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00117 · Cited by 89 · see citing works
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
Couture, H. D., Marron, J. S., Perou, C. M., Troester, M. A., & Niethammer, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_29 · Cited by 74 · see citing works
Prelaj, A., Rebuzzi, S. E., Pizzutilo, P., Bilancia, M., Montrone, M., Pesola, F., Longo, V., Bene, G. D., Lapadula, V., Cassano, F., et al. (2020) · Clinical Lung Cancer · doi:10.1016/j.cllc.2019.11.017 · Cited by 70 · see citing works
Self-citationSuh, J., Yoo, S., Park, J., Cho, S. Y., Cho, M. C., Son, H., & Jeong, H. (2020) · BJU International · doi:10.1111/bju.15122 · Cited by 45 · see citing works
Yang, G., Raschke, F., Barrick, T. R., & Howe, F. A. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25447 · Cited by 44 · see citing works
Siah, K. W., Khozin, S., Wong, C. H., & Lo, A. W. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00046 · Cited by 33 · see citing works
Rebuzzi, S. E., Prelaj, A., Friedlaender, A., Cortellini, A., Addeo, A., Genova, C., Naqash, A. R., Auclin, É., Mezquita, L., & Banna, G. L. (2022) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2022.103806 · Cited by 32 · see citing works
Self-citationThe natural language explanation algorithms for the lung cancer computer-aided diagnosis system
Meldo, A., Utkin, L., Kovalev, M., & Kasimov, E. (2020) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2020.101952 · Cited by 31 · see citing works
Prelaj, A., Boeri, M., Robuschi, A., Ferrara, R., Proto, C., Russo, G. L., Galli, G., Toma, A. D., Brambilla, M., Occhipinti, M. A., et al. (2022) · Cancers · doi:10.3390/cancers14020435 · Cited by 31 · see citing works
Self-citationHan, S., Yang, X., Zhou, Q., Jing, Z., & Wu, W. (2020) · Cancer Medicine · doi:10.1002/cam4.3289 · Cited by 28 · see citing works
Li, F., & Dong, X. (2021) · Thoracic Cancer · doi:10.1111/1759-7714.14193 · Cited by 26 · see citing works
Hyperprogressive Disease upon Immune Checkpoint Blockade: Focus on Non–small Cell Lung Cancer
Russo, G. L., Facchinetti, F., Tiseo, M., Garassino, M. C., & Ferrara, R. (2020) · Current Oncology Reports · doi:10.1007/s11912-020-00908-9 · Cited by 22 · see citing works
Self-citationZhao, L. P., & Bolouri, H. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.001 · Cited by 19 · see citing works
Almars, A. M., Alwateer, M. M., Qaraad, M., Amjad, S., Fathi, H., Kelany, A. K., Hussein, N. K., & Elhosseini, M. A. (2021) · Diagnostics · doi:10.3390/diagnostics11101936 · Cited by 13 · see citing works
Lü, M., Wu, K.-H., Trudeau, S., Jiang, M., Zhao, J., & Fan, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-77653-3 · Cited by 11 · see citing works
Feature Selection of Breast Cancer Data Using Gradient Boosting Techniques of Machine Learning
Derangula, A., Edara, S. R., & Karri, P. K. (2020) · European Journal of Molecular & Clinical Medicine · Cited by 11 · see citing works
Weakly-Supervised Learning-Based Feature Localization in Confocal Laser Endomicroscopy Glioma ImagesPreprint
Izadyyazdanabadi, M., Belykh, E. G., Cavallo, C., Zhao, X., Gandhi, S., Moreira, L. B., Eschbacher, J. M., Nakaji, P., Preul, M. C., & Yang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.09428 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review
Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.125 · Cited by 340 · see citing works
Self-citationThe artificial intelligence and machine learning in lung cancer immunotherapy
Gao, Q., Yang, L., Lu, M., Jin, R., Ye, H., & Ma, T. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01456-y · Cited by 148 · see citing works
Pahud de Mortanges, A., Luo, H., Shu, S. Z., Kamath, A., Suter, Y., Shelan, M., Pöllinger, A., & Reyes, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01190-w · Cited by 93 · see citing works
Toumaj, S., Heidari, A., & Navimipour, N. J. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103243 · Cited by 36 · see citing works
Duwe, G., Mercier, D., Wiesmann, C., Kauth, V., Moench, K., Junker, M., Neumann, C., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Cancer Medicine · doi:10.1002/cam4.7398 · Cited by 31 · see citing works
Bhatt, J., Jain, S., Gupta, S. K., & Bhatia, D. D. (2025) · Nanoscale Advances · doi:10.1039/d5na00390c · Cited by 25 · see citing works
Wen, Q., Qiu, L., Qiu, C., Che, K., Zeng, R., Wang, X., Cao, P., Xing, L., Yang, Z., & Yu, J. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217881 · Cited by 23 · see citing works
Amoozgar, B., Bangolo, A., Habibi, M., Cho, C., & Goy, A. H. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26115319 · Cited by 21 · see citing works
Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer
Huang, L., Wu, X., You, J., Jin, Z., He, W., Sun, J., Shen, H., Liu, X., Yue, X., Cai, W., et al. (2025) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-24-1270 · Cited by 21 · see citing works
A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer
Kotoulas, S., Spyratos, D., Porpodis, Κ., Domvri, K., Boutou, A., Kaimakamis, E., Mouratidou, C., Alevroudis, I., Dourliou, V., Tsakiri, K., et al. (2025) · Cancers · doi:10.3390/cancers17050882 · Cited by 16 · see citing works
Sinha, T., Khan, A., Awan, M., Bokhari, S. F. H., Ali, K., Amir, M., Jadhav, A. N., Bakht, D., Puli, S. T., & Burhanuddin, M. (2024) · Cureus · doi:10.7759/cureus.61220 · Cited by 14 · see citing works
Mapping intratumor heterogeneity across layers for advancing immunotherapy
Marine, J., Bartok, O., Sagie, S., Vitiello, P. P., Bardelli, A., Weller, C., Chang, T., Tonelli, C., Spranger, S., Ruppin, E., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.025 · Cited by 13 · see citing works
Adytia, G. J., Sutanto, H., Pratiwi, L., & Fetarayani, D. (2025) · Immuno · doi:10.3390/immuno5010006 · Cited by 13 · see citing works
Li, C., Zhou, Z., Hou, L., Hu, K., Wu, Z., Xie, Y., Ouyang, J., & Cai, X. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108638 · Cited by 11 · see citing works
RetractedQiao, Y., Xie, D., Li, Z., Cao, S., & Zhao, D. (2025) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2435598 · Cited by 11 · see citing works
A hybrid AI method for lung cancer classification using explainable AI techniques
Shivwanshi, R. R., & Nirala, N. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.104985 · Cited by 12 · see citing works
Malizia, A., McGovern, J., Sarigiannis, D. A., Karakitsios, S., Anastas, P. T., Ludovici, G. M., Manenti, G., & Vasiliou, V. (2025) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-025-07030-4 · Cited by 7 · see citing works
Routine perioperative blood tests predict survival of resectable lung cancer
Morelli, D., Cantarutti, A., Valsecchi, C., Sabia, F., Rolli, L., Leuzzi, G., Bogani, G., & Pastorino, U. (2023) · Scientific Reports · doi:10.1038/s41598-023-44308-y · Cited by 7 · see citing works
Explainable artificial intelligence for multi-modal cancer analysis: from genomics to immunology
Zhang, M., & Yin, L. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105040 · Cited by 5 · see citing works
Chakramurty, A., Dinni, A. R., Silvia, I., Cantika, A. L., & Wungu, C. D. K. (2025) · ADMET & DMPK · doi:10.5599/admet.2956 · Cited by 3 · see citing works
Chen, S., Nie, R., Shen, X., Wang, Y., Luan, H., Zeng, X., Chen, Y., & Yuan, H. (2025) · Arthritis Research & Therapy · doi:10.1186/s13075-025-03477-z · Cited by 2 · see citing works
Dirner, A., Kormos, D., Lakatos, D., Bolyácz, M., Kocsis-Steinbach, M., Kalmár, G. G., Tihanyi, D., Takács, Á., Boldizsár, Á., Kardos, V., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00943-4 · Cited by 2 · see citing works
APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer
Prelaj, A., Provenzano, L., Miskovic, V., Ganzinelli, M., Mazzeo, L., Gemelli, M., Silvestri, C., Spagnoletti, A., Romanò, R., Brambilla, M., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01295-3 · Cited by 2 · see citing works
Self-citationImmunotherapy modeling: Harnessing the immune system in chronic disease treatment
Mishra, P., Khan, A., & Tungare, K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33877-9.00008-2 · Cited by 1 · see citing works
Artificial intelligence in lung cancer: from diagnosis to therapy
Wang, Y., Miao, L., & Zhou, C. (2025) · Current Opinion in Oncology · doi:10.1097/cco.0000000000001199 · Cited by 1 · see citing works
Asmara, O. D., Steenhuis, E. G. M., de Jong, K., Joseph, A., Timmer, M., Tenda, E. D., Boerma, E. C., Heuvelmans, M. A., & van Geffen, W. H. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250331 · Cited by 1 · see citing works
Application of artificial intelligence in immuno-oncology
Monfort‐Lanzas, P., Gronauer, R., Balaz, M., Floriani, G., & Hackl, H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14064-8.00017-5 · Cited by 1 · see citing works
Zheng, K., Zhang, J., Tingting, X., Li, F., Li, F., Zeng, J., Guo, Y., & Hao, Z. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1508721 · Cited by 1 · see citing works
Duwe, G., Mercier, D., Wiesmann, C., Junker, M., Haferkamp, A., Dengel, A., & Höfner, T. (2024) · Springer eBooks · doi:10.1007/978-3-658-43860-9_36
Zec, A., Declich, M. M., Sacco, M., Trovò, F., Lorenzini, D., Prelaj, A., & Miskovic, V. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96235-6_1
Self-citationSong, J., Kim, J., Lee, H., Min, R., & Chae, Y. K. (2025) · medRxiv · doi:10.64898/2025.12.03.25341593
Drug Discovery Using Biochip Technology
Tariq, Y., & Pindoo, I. A. (2026) · doi:10.1002/9781394360796.ch5
Cancer immunotherapy: Pros, cons, and beyond
Natarajan, K., Natarajan, K., Rajaram, G., Arulsamy, S., & Babu, D. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41551-7.00007-2
Khan, S. R., Khan, A., Dawood, T., Mestiri, S., Tareen, A., Farid, A. A. M., Raza, A., & Moosajee, M. (2026) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-026-02424-6
Nosa-Ihaza, E. A., Edeh, E. C., Wol, W. B. G., & Okenwa, E. (2026) · Journal of the Egyptian National Cancer Institute · doi:10.1186/s43046-026-00371-w
AI in hematology: A systematic review of techniques for rare blood disorder detection and treatment
Yermal, V. M., Manasa, Prabhu, D. R., Shetty, D. K., Girisha, S., & Bhat, S. H. (2026) · AIP conference proceedings · doi:10.1063/5.0327677
Enhancing Fair Play in Fantasy Sports: The Role of Explainable AI in Team Selection
Babu, P. V., Rao, M. V. P. C. S., Ketha, S., Sudhakar, G., Surada, A., & Hota, C. P. P. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28594-2_11
Jammoul, D., Ajibuwa, F., Jammoul, M., Schlosser‐Hupf, S., Müller, M., & El‐Najjar, N. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1784272
Sridharan, K., Fiala, O., Sivaramakrishnan, G., Matrana, M. R., Büttner, T., Kucharz, J., Cerrillo, J. M., Giannatempo, P., Myint, Z. W., de Liaño, A. G., et al. (2026) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2026.2727098
- Authors
- Prelaj, A., Galli, E. G., Miskovic, V., Pesenti, M., Viscardi, G., Pedica, B., Mazzeo, L., Bottiglieri, A., Provenzano, L., Spagnoletti, A., Marinacci, R., De Toma, A., Proto, C., Ferrara, R., Brambilla, M., Occhipinti, M., Manglaviti, S., Galli, G., Signorelli, D., Giani, C., Beninato, T., Pircher, C. C., Rametta, A., Kosta, S., Zanitti, M., Di Mauro, M. R., Rinaldi, A., Di Gregorio, S., Antonia, M., Garassino, M. C., de Braud, F. G. M., Restelli, M., Lo Russo, G., Ganzinelli, M., Trovò, F., & Pedrocchi, A. L. G.
- Affiliations
- Fondazione Politecnico di Milano (IT)
- University of Campania "Luigi Vanvitelli" (IT)
- University of Milan (IT)
- Fondazione IRCCS Istituto Nazionale dei Tumori (IT)
- Policlinico San Matteo Fondazione (IT)
- Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda (IT)
- Aalborg University (DK)
- University of Chicago (US)
- Corresponding author
- Prelaj, Arsela
- ORCID
- Prelaj, Arsela
- Miskovic, Vanja
- Pesenti, Mattia
- Viscardi, Giuseppe
- Mazzeo, Laura
- Bottiglieri, Achille
- Provenzano, Leonardo
- Spagnoletti, Andrea
- Proto, Claudia
- Ferrara, Roberto
- Brambilla, Marta
- Occhipinti, Mario
- Manglaviti, Sara
- Galli, Giulia
- Signorelli, Diego
- Giani, Claudia
- Beninato, Teresa
- Pircher, Chiara Carlotta
- Rametta, Alessandro
- Kosta, Sokol
- Rinaldi, Arturo
- Garassino, Marina Chiara
- Restelli, Marcello
- Ganzinelli, Monica
- Trovò, Francesco
- Pedrocchi, Alessandra Laura Giulia
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2023-01-23
- Volume
- 12
- Pages
- 1078822-1078822
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC9899835
- OpenAlex
- W4317718004
- Cited by
- 39
- References
- 35
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 6 · 2023: 3
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Oncology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion, cc-by)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Inflammatory Biomarkers in Disease Prognosis
- Keywords
- non-small cell lung cancer, immunotherapy response prediction, immune checkpoint inhibitors, explainable AI, SHAP, CatBoost, real-world data, neutrophil-to-lymphocyte ratio, PD-L1 expression, overall survival, disease control rate, chemoimmunotherapy
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
