As Cancers (ISSN: 2072-6694) is an open access journal, you have free and unlimited access to the full text of all articles. We welcome you to read our feature papers from the second half of 2024 in the “Cancer Informatics and Big Data” Section, which are listed below.

1. “Integrating Omics Data and AI for Cancer Diagnosis and Prognosis”
by Yousaku Ozaki, Phil Broughton, Hamed Abdollahi, Homayoun Valafar and Anna V. Blenda
Cancers 2024, 16(13), 2448; https://doi.org/10.3390/cancers16132448
Available online: https://www.mdpi.com/2072-6694/16/13/2448

2. “Learning from Imbalanced Data: Integration of Advanced Resampling Techniques and Machine Learning Models for Enhanced Cancer Diagnosis and Prognosis”
by Fatih Gurcan and Ahmet Soylu
Cancers 2024, 16(19), 3417; https://doi.org/10.3390/cancers16193417
Available online: https://www.mdpi.com/2072-6694/16/19/3417

3. “Predicting Biochemical Recurrence of Prostate Cancer Post-Prostatectomy Using Artificial Intelligence: A Systematic Review”
by Jianliang Liu, Haoyue Zhang, Dixon T. S. Woon, Marlon Perera and Nathan Lawrentschuk
Cancers 2024, 16(21), 3596; https://doi.org/10.3390/cancers16213596
Available online: https://www.mdpi.com/2072-6694/16/21/3596

4. “Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features”
by Domiziana Santucci, Raffaele Ragone, Elva Vergantino, Federica Vaccarino, Francesco Esperto, Francesco Prata, Roberto Mario Scarpa, Rocco Papalia, Bruno Beomonte Zobel, Francesco Rosario Grasso et al.
Cancers 2024, 16(15), 2731; https://doi.org/10.3390/cancers16152731
Available online: https://www.mdpi.com/2072-6694/16/15/2731

5. “Lung Cancer and Air Quality in a Large Urban County in the United States”
by Hollis Hutchings, Qiong Zhang, Sue C. Grady, Jessica Cox, Andrew Popoff, Carl P. Wilson, Shangrui Zhu and Ikenna Okereke
Cancers 2024, 16(11), 2146; https://doi.org/10.3390/cancers16112146
Available online: https://www.mdpi.com/2072-6694/16/11/2146

6. “A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity”
by Menghan Wang, Yanqi Xie, Jinpeng Liu, Austin Li, Li Chen, Arnold Stromberg, Susanne M. Arnold, Chunming Liu and Chi Wang
Cancers 2024, 16(13), 2488; https://doi.org/10.3390/cancers16132488
Available online: https://www.mdpi.com/2072-6694/16/13/2488

7. “The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning”
by Michele Avanzo, Joseph Stancanello, Giovanni Pirrone, Annalisa Drigo and Alessandra Retico
Cancers 2024, 16(21), 3702; https://doi.org/10.3390/cancers16213702
Available online: https://www.mdpi.com/2072-6694/16/21/3702

8. “Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion”
by Navid Sobhani, Alberto D’Angelo, Matteo Pittacolo, Giuseppina Mondani and Daniele Generali
Cancers 2024, 16(17), 3089; https://doi.org/10.3390/cancers16173089
Available online: https://www.mdpi.com/2072-6694/16/17/3089

9. “Testing and Validation of a Custom Retrained Large Language Model for the Supportive Care of HN Patients with External Knowledge Base”
by Libing Zhu, Yi Rong, Lisa A. McGee, Jean-Claude M. Rwigema and Samir H. Patel
Cancers 2024, 16(13), 2311; https://doi.org/10.3390/cancers16132311
Available online: https://www.mdpi.com/2072-6694/16/13/2311

10. “Explainable Thyroid Cancer Diagnosis Through Two-Level Machine Learning Optimization with an Improved Naked Mole-Rat Algorithm”
by Wojciech Książek
Cancers 2024, 16(24), 4128; https://doi.org/10.3390/cancers16244128
Available online: https://www.mdpi.com/2072-6694/16/24/4128

11. “Synthetic Boosted Resampling Using Deep Generative Adversarial Networks: A Novel Approach to Improve Cancer Prediction from Imbalanced Datasets”
by Fatih Gurcan and Ahmet Soylu
Cancers 2024, 16(23), 4046; https://doi.org/10.3390/cancers16234046
Available online: https://www.mdpi.com/2072-6694/16/23/4046

12. “The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review”
by Gauhar Dunenova, Zhanna Kalmataeva, Dilyara Kaidarova, Nurlan Dauletbaev, Yuliya Semenova, Madina Mansurova, Andrej Grjibovski, Fatima Kassymbekova, Aidos Sarsembayev, Daniil Semenov et al.
Cancers 2024, 16(15), 2761; https://doi.org/10.3390/cancers16152761
Available online: https://www.mdpi.com/2072-6694/16/15/2761

13. “Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival”
by Jesse Bucksot, Katherine Ritchie, Matthew Biancalana, John A. Cole and Daniel Cook
Cancers 2024, 16(13), 2302; https://doi.org/10.3390/cancers16132302
Available online: https://www.mdpi.com/2072-6694/16/13/2302

14. “Real-World Analysis of Survival and Treatment Efficacy in Stage IIIA-N2 Non-Small Cell Lung Cancer”
by Eleni Josephides, Roberta Dunn, Annie-Rose Henry, John Pilling, Karen Harrison-Phipps, Akshay Patel, Shahreen Ahmad, Michael Skwarski, James Spicer, Alexandros Georgiou et al.
Cancers 2024, 16(17), 3058; https://doi.org/10.3390/cancers16173058
Available online: https://www.mdpi.com/2072-6694/16/17/3058 

15. “Deep Neural Network Integrated into Network-Based Stratification (D3NS): A Method to Uncover Cancer Subtypes from Somatic Mutations”
by Matteo Valerio, Alessandro Inno, Alberto Zambelli, Laura Cortesi, Domenica Lorusso, Valeria Viassolo, Matteo Verzè, Fabrizio Nicolis and Stefania Gori
Cancers 2024, 16(16), 2845; https://doi.org/10.3390/cancers16162845
Available online: https://www.mdpi.com/2072-6694/16/16/2845

You can view and submit relevant papers to Cancers via https://www.mdpi.com/journal/cancers.

Cancers Editorial Office