Cancers | Feature Papers from the First Half of 2024 in the Section “Cancer Informatics and Big Data”
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 first half of 2024 in the Section “Cancer Informatics and Big Data”, which are listed below.
1. “Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics–Clinicopathological Nomogram”
by Heng Jia, Ruzhi Li, Yawei Liu, Tian Zhan, Yuan Li and Jianping Zhang
Cancers 2024, 16(3), 614; https://doi.org/10.3390/cancers16030614
Available online: https://www.mdpi.com/2072-6694/16/3/614
2. “Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning”
by Guillaume Mestrallet
Cancers 2024, 16(2), 408; https://doi.org/10.3390/cancers16020408
Available online: https://www.mdpi.com/2072-6694/16/2/408
3. “Artificial Intelligence-Based Management of Adult Chronic Myeloid Leukemia: Where Are We and Where Are We Going?”
by Simona Bernardi, Mauro Vallati and Roberto Gatta
Cancers 2024, 16(5), 848; https://doi.org/10.3390/cancers16050848
Available online: https://www.mdpi.com/2072-6694/16/5/848
4. “Metabolomics, Transcriptome and Single-Cell RNA Sequencing Analysis of the Metabolic Heterogeneity between Oral Cancer Stem Cells and Differentiated Cancer Cells”
by Yuwen Miao, Pan Wang, Jinyan Huang, Xin Qi, Yingjiqiong Liang, Wenquan Zhao, Huiming Wang, Jiong Lyu and Huiyong Zhu
Cancers 2024, 16(2), 237; https://doi.org/10.3390/cancers16020237
Available online: https://www.mdpi.com/2072-6694/16/2/237
5. “Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer”
by Jonghyun Lee, Seunghyun Cha, Jiwon Kim, Jung Joo Kim, Namkug Kim, Seong Gyu Jae Gal, Ju Han Kim, Jeong Hoon Lee, Yoo-Duk Choi, Sae-Ryung Kang et al.
Cancers 2024, 16(2), 430; https://doi.org/10.3390/cancers16020430
Available online: https://www.mdpi.com/2072-6694/16/2/430
6. “Individual Survival Distributions Generated by Multi-Task Logistic Regression Yield a New Perspective on Molecular and Clinical Prognostic Factors in Gastric Adenocarcinoma”
by Daniel Skubleny, Jennifer Spratlin, Sunita Ghosh, Russell Greiner, Daniel E. Schiller and Gina R. Rayat
Cancers 2024, 16(4), 786; https://doi.org/10.3390/cancers16040786
Available online: https://www.mdpi.com/2072-6694/16/4/786
7. “scRNAseq and High-Throughput Spatial Analysis of Tumor and Normal Microenvironment in Solid Tumors Reveal a Possible Origin of Circulating Tumor Hybrid Cells”
by Abdullah Mahmood Ali and Azra Raza
Cancers 2024, 16(7), 1444; https://doi.org/10.3390/cancers16071444
Available online: https://www.mdpi.com/2072-6694/16/7/1444
8. “Clinical and Diagnostic Utility of Genomic Profiling for Digestive Cancers: Real-World Evidence from Japan”
by Marin Ishikawa, Kohei Nakamura, Ryutaro Kawano, Hideyuki Hayashi, Tatsuru Ikeda, Makoto Saito, Yo Niida, Jiichiro Sasaki, Hiroyuki Okuda, Satoshi Ishihara et al.
Cancers 2024, 16(8), 1504; https://doi.org/10.3390/cancers16081504
Available online: https://www.mdpi.com/2072-6694/16/8/1504
9. “Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types”
by Tikam Chand Dakal, Nancy George, Caiming Xu, Prashanth Suravajhala and Abhishek Kumar
Cancers 2024, 16(9), 1626; https://doi.org/10.3390/cancers16091626
Available online: https://www.mdpi.com/2072-6694/16/9/1626
You can view and submit relevant papers to Cancers via https://www.mdpi.com/journal/cancers.
Cancers Editorial Office