RadioPathomics: Multimodal learning in non-small cell lung cancer for adaptive radiotherapy
Open accessAbstract
Current practice in cancer treatment collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent rise of radiomics and pathomics, i.e., the extraction of quantitative features from radiology and histopathology images collected to predict clinical outcomes or guide clinical decisions using artificial intelligence algorithms. Nevertheless, how to combine them into a single multimodal framework is still an open issue. In this work, we develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiotherapy treatment outcomes for non-small-cell lung cancer patients. Within this context, we investigate eight different late fusion rules and two patient-wise aggregation rules leveraging the richness of information given by CT images, whole-slide scans and clinical data. The experiments in leave-one-patient-out cross-validation on an in-house cohort of 33 patients show that the proposed fusion-based multimodal paradigm, with an AUC equal to 90.9%, outperforms each unimodal approach, suggesting that data integration can advance precision medicine. The results also show that late fusion favourably compares against early fusion, another commonly used multimodal approach. As a further contribution, we explore the chance to use a deep learning framework against hand-crafted features. In our scenario characterised by different modalities and a limited amount of data, as it may happen in different areas of cancer research, the results show that the latter is still a viable and effective option for extracting relevant information with respect to the former.
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?
A key issue with prior multimodal AI applications using handcrafted features was that difficulties requiring particular domain expertise were multiplied, as expert clinicians would also need to be involved in the feature design phase.
“A key issue with these approaches was that the difficulties requiring particular domain expertise are multiplied, as expert clinicians would also need to be involved in the feature design phase (Vaidya et al., 2020; Tortora et al., 2023).”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Tortora et al., 2023
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398 · see citing works
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882 · see citing works
Ho, T. K. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1995.598994 · Cited by 6,311 · see citing works
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599 · see citing works
Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge
Tomczak, K. J., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,503 · see citing works
Kuncheva, L. I. (2004) · Wiley eBooks · doi:10.1002/0471660264 · Cited by 3,007 · see citing works
API design for machine learning software: experiences from the scikit-learn projectPreprint
Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., et al. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.0238 · Cited by 2,387 · see citing works
Postmus, P. E., Kerr, K. M., Oudkerk, M., Senan, S., Waller, D. A., Vansteenkiste, J., Escriu, C., & Peters, S. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx222 · Cited by 2,301 · see citing works
Radiomics: the process and the challenges
Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.010 · Cited by 2,258 · cited 1× · see citing works
“• Radiomics: it is based on the extraction of quantitative features from radiology images routinely collected in order to predict clinical outcomes or guide clinical decisions using AI algorithms [3, 4].”
(2011) · Princeton University Press eBooks · doi:10.2307/j.ctvcm4g18.8 · Cited by 1,197 · see citing works
Deep multimodal learning: a survey on recent advances and trends
Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139 · cited 3× · see citing works
“Definitely, the late fusion approach is a well-suited multimodal strategy when input modalities are significantly uncorrelated and they vary significantly in terms of data dimensionality and sampling rates [12].”
Decision templates for multiple classifier fusion: an experimental comparison
Kuncheva, L. I., Bezdek, J. C., & Duin, R. P. W. (2001) · Pattern Recognition · doi:10.1016/s0031-3203(99)00223-x · Cited by 1,009 · see citing works
Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
Deep neural network models for computational histopathology: A survey
Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101813 · Cited by 749 · see citing works
Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107161 · Cited by 734 · see citing works
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works
Kuncheva, L. I. (2014) · Wiley eBooks · doi:10.1002/9781118914564 · Cited by 670 · see citing works
An up-to-date comparison of state-of-the-art classification algorithms
Zhang, C., Liu, C., Zhang, X., & Almpanidis, G. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.003 · Cited by 440 · see citing works
Parameter tuning or default values? An empirical investigation in search-based software engineering
Arcuri, A., & Fraser, G. (2013) · Empirical Software Engineering · doi:10.1007/s10664-013-9249-9 · Cited by 411 · see citing works
Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife · doi:10.7554/elife.23421 · Cited by 365 · see citing works
Lee, G., Lee, H. Y., Park, H., Schiebler, M. L., van Beek, E. J. R., Ohno, Y., Seo, J. B., & Leung, A. N. C. (2016) · European Journal of Radiology · doi:10.1016/j.ejrad.2016.09.005 · Cited by 277 · see citing works
First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images
Aggarwal, N., & Agrawal, R. K. (2012) · Journal of Signal and Information Processing · doi:10.4236/jsip.2012.32019 · Cited by 210 · see citing works
Efficient Large-Scale Multi-Modal Classification
Kiela, D., Grave, E., Joulin, A., & Mikolov, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11945 · Cited by 177 · see citing works
Application of Deep Learning in Breast Cancer Imaging
Balkenende, L., Teuwen, J., & Mann, R. M. (2022) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2022.02.003 · Cited by 174 · see citing works
Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y. (2022) · IRBM · doi:10.1016/j.irbm.2020.12.002 · Cited by 137 · see citing works
Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey
Monkam, P., Qi, S., Ma, H., Gao, W., Yao, Y., & Qian, W. (2019) · IEEE Access · doi:10.1109/access.2019.2920980 · Cited by 131 · see citing works
Gupta, R., Kurç, T., Sharma, A., Almeida, J. S., & Saltz, J. H. (2019) · Current Pathobiology Reports · doi:10.1007/s40139-019-00200-x · Cited by 129 · see citing works
Radiomics in prostate cancer: an up-to-date review
Ferro, M., COBELLI, O. D., Musi, G., Giudice, F. D., Carrieri, G., Busetto, G. M., Falagario, U. G., Sciarra, A., Maggi, M., Crocetto, F., et al. (2022) · Therapeutic Advances in Urology · doi:10.1177/17562872221109020 · Cited by 123 · see citing works
Braman, N., Gordon, J. W. H., Goossens, E. T., Willis, C., Stumpe, M. C., & Venkataraman, J. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87240-3_64 · Cited by 107 · see citing works
Combining diversity measures for ensemble pruning
Cavalcanti, G. D. C., Oliveira, L. S., Moura, T., & Carvalho, G. V. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.01.029 · Cited by 98 · see citing works
Soda, P., D’Amico, N. C., Tessadori, J., Valbusa, G., Guarrasi, V., Bortolotto, C., Akbar, M. U., Sicilia, R., Cordelli, E., Fazzini, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102216 · Cited by 92 · see citing works
Self-citationZhang, Y., He, K., Guo, Y., Liu, X.-C., Yang, Q., Zhang, C.-Y., Xie, Y., Mu, S., Guo, Y., Fu, Y., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00457 · Cited by 64 · see citing works
A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients
Ramella, S., Fiore, M., Greco, C., Cordelli, E., Sicilia, R., Merone, M., Molfese, E., Miele, M., Cornacchione, P., Ippolito, E., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207455 · Cited by 64 · see citing works
Self-citationAbdel-Nabi, H., Ali, M. Z., Awajan, A., Daoud, M. I., Alazrai, R., Suganthan, P. N., & Ali, T. Z. (2023) · Cluster Computing · doi:10.1007/s10586-022-03951-2 · Cited by 50 · see citing works
LBP features for breast cancer detection
Král, P., & Lenc, L. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532838 · Cited by 47 · see citing works
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis
Wu, Y., Ma, J., Huang, X., Ling, S. H., & Su, S. W. (2021) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc52423.2021.9658891 · Cited by 39 · see citing works
Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM
Chaddad, A., Zinn, P. O., & Colen, R. R. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163822 · Cited by 38 · see citing works
Cordelli, E., Maulucci, G., Spirito, M. D., Rizzi, A., Pitocco, D., & Soda, P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.025 · Cited by 31 · see citing works
Self-citationRashmi, R., Prasad, K., Udupa, C. B. K., & Shwetha, V. (2020) · IEEE Access · doi:10.1109/access.2020.2984522 · Cited by 22 · see citing works
Joo, L., Jung, S. C., Lee, H., Park, S. Y., Kim, M., Park, J. E., & Choi, K. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-96621-z · Cited by 17 · see citing works
Radiomics-Based Prediction of Overall Survival in Lung Cancer Using Different Volumes-Of-Interest
D’Amico, N. C., Sicilia, R., Cordelli, E., Tronchin, L., Greco, C., Fiore, M., Carnevale, A., Iannello, G., Ramella, S., & Soda, P. (2020) · Applied Sciences · doi:10.3390/app10186425 · Cited by 16 · see citing works
Self-citationSicilia, R., Cordelli, E., Merone, M., Luperto, E., Papalia, R., Iannello, G., & Soda, P. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00078 · Cited by 10 · see citing works
Self-citationExploring Deep Pathomics in Lung Cancer
Liu, C. Z., Sicilia, R., Tortora, M., Cordelli, E., Nibid, L., Sabarese, G., Perrone, G., Fiore, M., Ramella, S., & Soda, P. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00092 · Cited by 9 · see citing works
Self-citationTackling imbalance radiomics in acoustic neuroma
D', N. C., Amico, N. A., Merone, M., Sicilia, R., Cordelli, E., D’Antoni, F., Antoni, N. A., Zanetti, I. B., Valbusa, G., Grossi, E., et al. (2019) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2019.101396 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Multimodal data integration for oncology in the era of deep neural networks: a review
Waqas, A., Tripathi, A., Ramachandran, R. P., Stewart, P. A., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1408843 · Cited by 165 · see citing works
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 108 · see citing works
A review of cancer data fusion methods based on deep learning
Zhao, Y., Li, X., Zhou, C., Peng, H., Zheng, Z., Chen, J., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102361 · Cited by 86 · see citing works
Multimodal Artificial Intelligence in Medical Diagnostics
Jandoubi, B., & Akhloufi, M. A. (2025) · Information · doi:10.3390/info16070591 · Cited by 88 · see citing works
Extending the landscape of omics technologies by pathomics
Bülow, R. D., Hölscher, D. L., Costa, I. G., & Boor, P. (2023) · npj Systems Biology and Applications · doi:10.1038/s41540-023-00301-9 · Cited by 76 · see citing works
Transformer-Based Hierarchical Model for Non-Small Cell Lung Cancer Detection and Classification
Imran, M., Haq, B., Α, E. E., Topcu, A. E., & Shao, W. (2024) · IEEE Access · doi:10.1109/access.2024.3449230 · Cited by 50 · see citing works
Caruso, C. M., Guarrasi, V., Cordelli, E., Sicilia, R., Gentile, S., Messina, L., Fiore, M., Piccolo, C. L., Zobel, B. B., Iannello, G., et al. (2022) · Journal of Imaging · doi:10.3390/jimaging8110298 · Cited by 31 · see citing works
Self-citationClass balancing diversity multimodal ensemble for Alzheimer’s disease diagnosis and early detection
Francesconi, A., di Biase, L., Cappetta, D., Rebecchi, F., Soda, P., Sicilia, R., & Guarrasi, V. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102529 · Cited by 30 · see citing works
Self-citationKhan, M. A., Ghazal, T. M., Saleem, M., Farooq, M. S., Yeun, C. Y., Ahmad, M., & Lee, S.-W. (2025) · Scientific Reports · doi:10.1038/s41598-025-19478-6 · Cited by 24 · see citing works
Deep learning-based multimodal spatial transcriptomics analysis for cancer
Rajdeo, P., Aronow, B. J., & Prasath, V. B. S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.08.001 · Cited by 23 · see citing works
Ai, Y., Liu, J., Li, Y., Wang, F., Du, X., Jain, R. K., Lin, L., & Chen, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3471194 · Cited by 20 · see citing works
Nibid, L., Greco, C., Cordelli, E., Sabarese, G., Fiore, M., Liu, C. Z., Ippolito, E., Sicilia, R., Miele, M., Tortora, M., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294259 · Cited by 18 · see citing works
Self-citationDeep learning for human locomotion analysis in lower-limb exoskeletons: a comparative study
Coser, O., Tamantini, C., Tortora, M., Furia, L., Sicilia, R., Zollo, L., & Soda, P. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1597143 · Cited by 10 · see citing works
Self-citationAI-Driven Personalized Healthcare: Leveraging Multimodal Data for Precision Medicine
Lin, R. (2024) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat62293.2024.00112 · Cited by 8 · see citing works
Bonci, E.-A., Bandura, A., Dooley, A., Erjan, A., Gebreslase, H. W., Hategan, M., Khanduja, D., Lai, E., Lescaie, A., Nițescu, G. V., et al. (2025) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.104929 · Cited by 11 · see citing works
Deshpande, P. S., Bhatt, M. W., Shinde, S. K., Labhade-Kumar, N., Ashokkumar, N., Venkatesan, K. G. S., & Shadrach, F. D. (2023) · Journal of Artificial Intelligence and Technology · doi:10.37965/jait.2023.0388 · Cited by 8 · see citing works
Mtoor, S., Rashidian, N., Messaoudi, N., Grasso, V., Noël, F., Steindler, M., Jaradat, D., Frigerio, I. M., Butturini, G., Croner, R., et al. (2026) · Bioengineering · doi:10.3390/bioengineering13010117 · Cited by 7 · see citing works
Brancato, V., Verdicchio, M., Cavaliere, C., Isgrò, F., Salvatore, M., & Aiello, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01841-8 · Cited by 6 · see citing works
Eftekharian, M., & Hashemi, Z. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02222-5 · Cited by 6 · see citing works
Alqahtani, N., Khan, A. A., Mahendran, R. K., & Faheem, M. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70039 · Cited by 5 · see citing works
Levy, J., Sakatani, T., Murakami, K., Kita, Y., Kobayashi, T., Win, S. S., Manoukian, S. B., Rosser, C. J., & Furuya, H. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26199570 · Cited by 4 · see citing works
Efficient Model for Prediction of Non-small Cells Lung Cancer via Deep Q-Learning
Narad, S., & Reddy, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9040-5_29 · Cited by 4 · see citing works
Lightweight multimodal Cycle-Attention Transformer towards cancer diagnosis
Liu, S., Ma, X., Deng, S., Suo, Y., Zhang, J., & Ng, W. W. Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124616 · Cited by 4 · see citing works
Şerbănescu, M.-S., Streba, L., Demetrian, A. D., Gheorghe, A.-G., Mămuleanu, M., Pirici, D. N., & Streba, C. T. (2025) · Cancers · doi:10.3390/cancers17040611 · Cited by 4 · see citing works
Nahmatwlla, L. L., & Ali, A. M. (2025) · ARO-The Scientific Journal of Koya University · doi:10.14500/aro.12355 · Cited by 3 · see citing works
Advances in artificial intelligence-based radiogenomics for lung cancer precision medicine
Sun, Y., Guo, X., Liu, X., Liu, H., & Wang, X. (2025) · Progress in Biomedical Engineering · doi:10.1088/2516-1091/ae224a · Cited by 2 · see citing works
Aharonu, M., & Ramasamy, L. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-29781-x · Cited by 2 · see citing works
Predicting lung cancer survival with attention-based CT slices combination
Paolo, D., Greco, C., Ippolito, E., Fiore, M., Ramella, S., Soda, P., Tortora, M., Bria, A., & Sicilia, R. (2026) · Health Information Science and Systems · doi:10.1007/s13755-025-00404-z · Cited by 2 · see citing works
Self-citationBiomarker discovery using multimodal data with the potential application in lung tumor diagnosis
Liu, R., Wang, K., Wang, S., & Zhang, Q. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385354 · Cited by 2 · see citing works
Probabilistic Integration of Renal Cancer Radiology and Pathology Using Graph Neural Networks
Gao, S., Gao, S., Machado, I. P., & Crispin‐Ortuzar, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_54 · Cited by 1 · see citing works
Artificial intelligence and radiomics in precision oncology
Shafeeque, M. A., Fatima, N., Khan, M. A., Rashid, G., Singh, T., Zaidi, N., & Siddiqui, Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00015-3 · Cited by 1 · see citing works
Mantegna, M., Ayllón, E. M., Caragliano, A. N., Feola, F. D., Tacconi, C., Fiore, M., Ippolito, E., Greco, C., Ramella, S., Cattin, P. C., et al. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102774 · Cited by 1 · see citing works
Self-citationAn Approach to Radiotherapy Treatment Planning based on Machine Learning Algorithms
Divya, K., Bhargavi, C. H., Abirami, M., Arjun, P., Padmaja, P., & Dhanwanth, B. (2024) · International Conference on Expert Clouds and Applications (ICOECA) · doi:10.1109/icoeca62351.2024.00143 · Cited by 1 · see citing works
Benchmarking GAN-Based vs Classical Data Augmentation on Biomedical Images
Mantegna, M., Tronchin, L., Tortora, M., & Soda, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87660-8_7 · Cited by 1 · see citing works
Self-citationDeep Learning Assisted model for Medical Image Fusion
Narad, S., & Reddy, K. (2024) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei61867.2024.10842837
OMM-RLC Optimised Multimodal Fusion For Recurrence Lung Cancer Detection
Subhash, P., Manju, D., Sreaya, V., Sharath, P., Shreyana, S., & Manideep, P. (2024) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla63077.2024.10871259
Advanced Techniques for Cancer Research with Multimodal Fusion and Deep Learning
Shankar, M., Kalinathan, L., & Murugan, J. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-82389-3_21
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
Ayllón, E. M., Mantegna, M., Shen, L., Soda, P., Guarrasi, V., & Tortora, M. (2025) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms65348.2025.00083
Self-citationArtificial Intelligence-Based Pathomics: Technological Features and Clinical Applications
Kang, L., Liu, L., Chen, H., Zheng, C., & Ye, T. (2025) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.1007/s44411-025-00436-7
Integrating Genomic Data and Imaging in Lung Cancer Prediction Using a Hybrid Deep Learning Approach
Sharma, A., & Kandoi, N. M. (2025) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000001288
Wu, X., Cao, R., Tan, Z., Wei, P., Su, Y., & Zheng, C. (2026) · Bioengineering · doi:10.3390/bioengineering13020142
Augasthega, R., & Vijayakumar, K. (2025) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem65670.2025.11374444
Bhuvaneswari, R., & Kaviya, S. (2025) · International Conference on Data, Energy and Communication Networks (DECoN) · doi:10.1109/decon67170.2025.11447557
Pathology-guided contrastive pretraining enriches preoperative CT representations for prognosis
Guo, Z., Harmanani, M., Wilson, P., To, M. N. N., Elghareb, T., Dzikunu, O. K., Maghsoodi, N., Gabor Fichtinger, A., Abolmaesumi, P., & Mousavi, P. (2026) · SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling · doi:10.1117/12.3086280
Artificial intelligence in cancer imaging
Richi, M., Khan, M., & Mujahid, M. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00012-8
Yin, Z., Shu, Y., Shen, D., Gao, H., Zhang, Y., Yang, Z., Gong, W., Chen, T., & Liu, Y. (2026) · BMC Cancer · doi:10.1186/s12885-026-16116-w
Mallah, E., Altarawneh, A., Bardees, R., Arzoky, M., Swift, S., & Mansoor, K. (2026) · International Conference on Computational Intelligence Approaches and Applications (ICCIAA) · doi:10.1109/icciaa68481.2026.11543842
Advancements in Medical Information Fusion: A Review of Deep Learning Models and Their Applications
Wu, H., Xu, Y., Zhang, Z., Jiang, D., & Li, H. (2026) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70120
Transformer-Based Multimodal Deep Learning Models for Early Stage Lung Cancer Detection
B, M. K., & Senthil Kumar, R. (2026) · doi:10.1109/incect72370.2026.11742591
- Authors
- Tortora, M., Cordelli, E., Sicilia, R., Nibid, L., Ippolito, E., Perrone, G., Ramella, S., & Soda, P.
- Affiliations
- Università Campus Bio-Medico (IT)
- ORCID
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 47563-47578
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4380980908
- Cited by
- 49
- References
- 49
- Citations by year
- 2026: 12 · 2025: 21 · 2024: 11 · 2023: 4 · 2022: 1
- FWCI
- 7.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DiVA at Umeå University (Umeå University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Università Campus Bio-Medico di Roma (PE0000013-FAIR)
- Regione Lazio (PO FSE 2014-2020)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- non-small cell lung cancer, adaptive radiotherapy, multimodal fusion, decision fusion, radiomics, pathomics, histopathology, CT images, clinical data, treatment outcome prediction, precision medicine, handcrafted features, deep learning model comparison
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
