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The Future of Machine Learning in Predicting the Treatment Responses...
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The Future of Machine Learning in Predicting the Treatment Responses of Cancers

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 11 July 2025 | Viewed by 4608

Special Issue Editors

Prof. Dr. Alfredo Vellido

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Guest Editor
IDEAI_UPC Research Center, Universitat Politècnica de Catalunya (UPC BarcelonaTech), 08034 Barcelona, Spain
Interests: machine learning; data science; artificial intelligence; cancer
Dr. Ana Candiota

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Guest Editor
1. Centro de Investigación Biomédica en Red: Bioingeniería, Biomateriales y Nanomedicina, 08193 Cerdanyola del Vallès, Spain
2. Departament de Bioquímica i Biologia Molecular, Facultat de Biociències, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
Interests: magnetic resonance spectroscopy; imaging biomarkers; preclinical tumour models; MR contrast agents; brain tumours; magnetic resonance imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Margarida Julia-Sape

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Guest Editor
Departament de Bioquímica i Biologia Molecular, Institut de Biotecnologia I de Biomedicina, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Valles, Spain
Interests: precision medicine; prospective evaluation; clinical trial; decision-support tool; added value; non-invasive biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

After decades of research, machine learning (ML), understood as a toolbox for data analysis in medical applications, is now widely recognized as useful but only scantly applied in real medical practice. Oncology is, arguably, one of the pioneering domains in medicine in the research and development of ML-based analytical strategies. This can be at least partially explained by the many successes achieved in the analysis of medical image. A cancer-related problem that still requires much research from this point of view is the prediction of treatment responses. This includes the investigation of responses to new and experimental drugs and often involves pre-clinical studies.

This Special Issue welcomes innovative research on the use of artificial intelligence (AI) in general and ML in particular (including deep learning) for the analysis of any type of data related to the general problem of the prediction of treatment responses in cancers. This would include studies in, amongst others, clinical and pre-clinical settings and pharmacology. Contributions on personalized medicine, explainable AI, multi-modal data analysis, or data visualization, applied to or involving treatment response measures such as progression-free survival, amongst other topics, are welcome.

Prof. Dr. Alfredo Vellido
Dr. Ana Candiota
Dr. Margarida Julia-Sape
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cancer treatment response
  • medical decision support systems
  • cancer outcome prediction
  • personalized medicine
  • pre-clinical models
  • artificial intelligence
  • machine learning
  • deep learning
  • progression-free survival
  • disease-free survival
  • event-free survival
  • overall survival
  • adverse event
  • quality of life
  • surrogate endpoint

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Published Papers (3 papers)

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15 pages, 736 KiB  
Article
Improving Lung Cancer Risk Prediction Using Machine Learning: A Comparative Analysis of Stacking Models and Traditional Approaches
by Huakang Tu, Yunfeng Zhao, Jiameng Cui, Wanzhu Lu, Gege Sun, Xiaohang Xu, Qingfeng Hu, Kejia Hu, Ming Wu and Xifeng Wu
Cancers 2025, 17(10), 1651; https://doi.org/10.3390/cancers17101651 - 13 May 2025
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Abstract
Background: Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung cancer risk based on epidemiological questionnaires, comparing them [...] Read more.
Background: Lung cancer is a leading cause of cancer-related mortality worldwide, often diagnosed in advanced stages, making early detection critical. This study aimed to evaluate the performance of various machine learning models in predicting lung cancer risk based on epidemiological questionnaires, comparing them with traditional logistic regression models. Methods: A retrospective case–control study was conducted using data from 5421 lung cancer cases and 10,831 matched controls. The dataset included a wide range of demographic, clinical, and behavioral risk factors from epidemiological questionnaires. We developed and compared multiple machine learning algorithms, including LightGBM and stacking ensemble models, alongside logistic regression for predicting lung cancer risk. Model performance was evaluated using accuracy, area under the curve (AUC), and recall. Results: The stacking model outperformed traditional logistic regression, achieving an AUC of 0.887 (0.870–0.903) compared to 0.858 (0.839–0.878) for logistic regression. LightGBM also performed well, with an AUC of 0.884 (0.867–0.901). The stacking model achieved an accuracy of 81.2%, with a recall of 0.755, higher than the logistic regression model’s accuracy of 79.4%. Compared to classical lung cancer prediction models (LLP and PLCO), the logistic regression and ML models improved AUC by 12% to 27%. Conclusions: Integrating machine learning models into lung cancer screening programs can significantly enhance early detection efforts. Machine learning approaches, such as LightGBM and stacking, offer improved accuracy and predictive power over traditional models. However, efforts to enhance model interpretability through explainable AI techniques are necessary for broader clinical adoption. Full article
(This article belongs to the Special Issue The Future of Machine Learning in Predicting the Treatment Responses of Cancers)
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11 pages, 6151 KiB  
Article
Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma
by Mert Karabacak, Shiv Patil, Zachary Charles Gersey, Ricardo Jorge Komotar and Konstantinos Margetis
Cancers 2024, 16(21), 3614; https://doi.org/10.3390/cancers16213614 - 26 Oct 2024
Cited by 2 | Viewed by 1647
Abstract
(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall [...] Read more.
(1) Background: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, with an aggressive disease course that requires accurate prognosis for individualized treatment planning. This study aims to develop and evaluate a radiomics-based machine learning (ML) model to estimate overall survival (OS) for patients with GBM using pre-treatment multi-parametric magnetic resonance imaging (MRI). (2) Methods: The MRI data of 865 patients with GBM were assessed, comprising 499 patients from the UPENN-GBM dataset and 366 patients from the UCSF-PDGM dataset. A total of 14,598 radiomic features were extracted from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. The UPENN-GBM dataset was used for model development (70%) and internal validation (30%), while the UCSF-PDGM dataset served as an external test set. The NGBoost Survival model was developed to generate continuous probability estimates as well as predictions for 6-, 12-, 18-, and 24-month OS. (3) Results: The NGBoost Survival model successfully predicted survival, achieving a C-index of 0.801 on internal validation and 0.725 on external validation. For 6-month OS, the model attained an AUROC of 0.791 (95% CI: 0.742–0.832) and 0.708 (95% CI: 0.654–0.748) for internal and external validation, respectively. (4) Conclusions: The radiomics-based ML model demonstrates potential to improve the prediction of OS for patients with GBM. Full article
(This article belongs to the Special Issue The Future of Machine Learning in Predicting the Treatment Responses of Cancers)
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25 pages, 4536 KiB  
Article
Comparing Performances of Predictive Models of Toxicity after Radiotherapy for Breast Cancer Using Different Machine Learning Approaches
by Maria Giulia Ubeira-Gabellini, Martina Mori, Gabriele Palazzo, Alessandro Cicchetti, Paola Mangili, Maddalena Pavarini, Tiziana Rancati, Andrei Fodor, Antonella del Vecchio, Nadia Gisella Di Muzio and Claudio Fiorino
Cancers 2024, 16(5), 934; https://doi.org/10.3390/cancers16050934 - 25 Feb 2024
Cited by 5 | Viewed by 1950
Abstract
Purpose. Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). Methods. The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was [...] Read more.
Purpose. Different ML models were compared to predict toxicity in RT on a large cohort (n = 1314). Methods. The endpoint was RTOG G2/G3 acute toxicity, resulting in 204/1314 patients with the event. The dataset, including 25 clinical, anatomical, and dosimetric features, was split into 984 for training and 330 for internal tests. The dataset was standardized; features with a high p-value at univariate LR and with Spearman ρ>0.8 were excluded; synthesized data of the minority were generated to compensate for class imbalance. Twelve ML methods were considered. Model optimization and sequential backward selection were run to choose the best models with a parsimonious feature number. Finally, feature importance was derived for every model. Results. The model’s performance was compared on a training–test dataset over different metrics: the best performance model was LightGBM. Logistic regression with three variables (LR3) selected via bootstrapping showed performances similar to the best-performing models. The AUC of test data is slightly above 0.65 for the best models (highest value: 0.662 with LightGBM). Conclusions. No model performed the best for all metrics: more complex ML models had better performances; however, models with just three features showed performances comparable to the best models using many (n = 13–19) features. Full article
(This article belongs to the Special Issue The Future of Machine Learning in Predicting the Treatment Responses of Cancers)
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