About the Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface to “Artificial Intelligence in Cancer Diagnosis and Therapy” . . . . . . . . . . . . . . . xiii
Henrik J. Michaely, Giacomo Aringhieri, Dania Cioni and Emanuele Neri
Current Value of Biparametric Prostate MRI with Machine-Learning or Deep-Learning in the
Detection, Grading, and Characterization of Prostate Cancer: A Systematic Review
Reprinted from: Diagnostics 2022, 12, 799, doi:10.3390/diagnostics12040799 . . . . . . . . . . . . . 1
Russell Frood, Matthew Clark, Cathy Burton, Charalampos Tsoumpas, Alejandro F. Frangi,
Fergus Gleeson, Chirag Patel, et al.
Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting
Outcome in Diffuse Large B-Cell Lymphoma
Reprinted from: Cancers 2022, 14, 1711, doi:10.3390/cancers14071711 . . . . . . . . . . . . . . . . 23
Stephan Forchhammer, Amar Abu-Ghazaleh, Gisela Metzler, Claus Garbe
and Thomas Eigentler
Development of an Image Analysis-Based Prognosis Score Using Google’s Teachable Machine
in Melanoma
Reprinted from: Cancers 2022, 14, 2243, doi:10.3390/cancers14092243 . . . . . . . . . . . . . . . . 37
Lifeng Xu, Chun Yang, Feng Zhang, Xuan Cheng, Yi Wei, Shixiao Fan, Minghui Liu, et al.
Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and
Validation of a Prediction Model
Reprinted from: Cancers 2022, 14, 2574, doi:10.3390/ cancers14112574 . . . . . . . . . . . . . . . . 49
Sara Merkaj, Ryan C. Bahar, Tal Zeevi, MingDe Lin, Ichiro Ikuta, Khaled Bousabarah,
Gabriel I. Cassinelli Petersen, et al.
Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting:
Challenges and Opportunities
Reprinted from: Cancers 2022, 14, 2623, doi:10.3390/cancers14112623 . . . . . . . . . . . . . . . . 65
Qiyi Hu, Guojie Wang, Xiaoyi Song, Jingjing Wan, Man Li, Fan Zhang, Qingling Chen, et al.
Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in
Nasopharyngeal Carcinoma
Reprinted from: Cancers 2022, 14, 3201, doi:10.3390/cancers14133201 . . . . . . . . . . . . . . . . 81
Yuki Ito, Takahiro Nakajima, Terunaga Inage, Takeshi Otsuka, Yuki Sata, Kazuhisa Tanaka,
Yuichi Sakairi, et al.
Prediction of Nodal Metastasis in Lung Cancer Using Deep Learning of Endobronchial
Ultrasound Images
Reprinted from: Cancers 2022, 14, 3334, doi:10.3390/cancers14143334 . . . . . . . . . . . . . . . . 93
Marco Bertolini, Valeria Trojani, Andrea Botti, Noemi Cucurachi, Marco Galaverni,
Salvatore Cozzi, Paolo Borghetti, et al.
Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell
Lung Cancer
Reprinted from: Curr. Oncol. 2022, 29, 410, doi:10.3390/curroncol29080410 . . . . . . . . . . . . . 105
Wei Zhao, Yingli Sun, Kaiming Kuang, Jiancheng Yang, Ge Li, Bingbing Ni,
Yingjia Jiang, et al.
ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from
Follow-Up CT Series
Reprinted from: Cancers 2022, 14, 3675, doi:10.3390/cancers14153675 . . . . . . . . . . . . . . . . 121
v
Jason C. Hsu, Phung-Anh Nguyen, Phan Thanh Phuc, Tsai-Chih Lo, Min-Huei Hsu,
Min-Shu Hsieh, Nguyen Quoc Khanh Le, et al.
Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for
Lung Cancer Survival
Reprinted from: Cancers 2022, 14, 5562, doi:10.3390/cancers14225562 . . . . . . . . . . . . . . . . 133
Shuchita Dhwiren Patel, Andrew Davies, Emma Laing, Huihai Wu, Jeewaka Mendis
and Derk-Jan Dijk
Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and
Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study
Reprinted from: Cancers 2023, 15, 503, doi:10.3390/cancers15020503 . . . . . . . . . . . . . . . . . 147
Bart M. de Vries, Sandeep S. V. Golla, Gerben J. C. Zwezerijnen, Otto S. Hoekstra,
Yvonne W. S. Jauw, Marc C. Huisman, Guus A. M. S. van Dongen, et al.
3D Convolutional Neural Network-Based Denoising of Low-Count Whole-Body
18F-Fluorodeoxyglucose and 89Zr-Rituximab PET Scans
Reprinted from: Diagnostics 2022, 12, 596, doi:10.3390/diagnostics12030596 . . . . . . . . . . . . . 169
Marco Solbiati, Tiziana Ierace, Riccardo Muglia, Vittorio Pedicini, Roberto Iezzi,
Katia M. Passera, Alessandro C. Rotilio, et al.
Thermal Ablation of Liver Tumors Guided by Augmented Reality: An Initial
Clinical Experience
Reprinted from: Cancers 2022, 14, 1312, doi:10.3390/cancers14051312 . . . . . . . . . . . . . . . . 183
Pavel Alekseevich Lyakhov, Ulyana Alekseevna Lyakhova
and Nikolay Nikolaevich Nagornov
System for the Recognizing of Pigmented Skin Lesions with Fusion and Analysis of
Heterogeneous Data Based on a Multimodal Neural Network
Reprinted from: Cancers 2022, 14, 1819, doi:10.3390/cancers14071819 . . . . . . . . . . . . . . . . 197
Antonio Melillo, Andrea Chirico, Giuseppe De Pietro, Luigi Gallo, Giuseppe Caggianese,
Daniela Barone, Michelino De Laurentiis, et al.
Virtual Reality Rehabilitation Systems for Cancer Survivors: A Narrative Review of
the Literature
Reprinted from: Cancers 2022, 14, 3163, doi:10.3390/cancers14133163 . . . . . . . . . . . . . . . . 223
Jesus A. Basurto-Hurtado, Irving A. Cruz-Albarran, Manuel Toledano-Ayala,
Mario Alberto Ibarra-Manzano, Luis A. Morales-Hernandez and Carlos A. Perez-Ramirez
Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification
Strategies Using Artificial Intelligence Algorithms
Reprinted from: Cancers 2022, 14, 3442, doi:10.3390/cancers14143442 . . . . . . . . . . . . . . . . 239
Ji-Sun Kim, Byung Guk Kim and Se Hwan Hwang
Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from
Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis
Reprinted from: Cancers 2022, 14, 3499, doi:10.3390/cancers14143499 . . . . . . . . . . . . . . . . 263
Diana Veiga-Canuto, Leonor Cerd`a-Alberich, Cinta Sang ¨uesa Nebot, Blanca Mart´ınez
de las Heras, Ulrike P¨otschger, Michela Gabelloni, Jos´e Miguel Carot Sierra, et al.
Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic
Segmentation of Neuroblastic Tumors in Magnetic Resonance Images
Reprinted from: Cancers 2022, 14, 3648, doi:10.3390/cancers14153648 . . . . . . . . . . . . . . . . 275
JaeYen Song, Soyoung Im, Sung Hak Lee and Hyun-Jong Jang
Deep Learning-Based Classification of Uterine Cervical and Endometrial Cancer Subtypes from
Whole-Slide Histopathology Images
Reprinted from: Diagnostics 2022, 12, 2623, doi:10.3390/diagnostics12112623 . . . . . . . . . . . . 291
vi
Bahrudeen Shahul Hameed and Uma Maheswari Krishnan
Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer
Reprinted from: Cancers 2022, 14, 5382, doi:10.3390/cancers14215382 . . . . . . . . . . . . . . . . 305
Victor I. J. Strijbis, Max Dahele, Oliver J. Gurney-Champion, Gerrit J. Blom,
Marije R. Vergeer, Berend J. Slotman and Wilko F. A. R. Verbakel
Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck
Cancer Radiotherapy
Reprinted from: Cancers 2022, 14, 5501, doi:10.3390/cancers14225501 . . . . . . . . . . . . . . . . 327
Faicel Chamroukhi, Segolene Brivet, Peter Savadjiev, Mark Coates and Reza Forghani
DECT-CLUST: Dual-Energy CT Image Clusteringand Application to Head and Neck Squamous
Cell Carcinoma Segmentation
Reprinted from: Cancers 2022, 12, 3072, doi:10.3390/diagnostics12123072 . . . . . . . . . . . . . . 345
Yinghong Guo, JiangfengWu, YunlaiWang and Yun Jin
Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying
HER2 Status in Patients with Breast Carcinoma
Reprinted from: Diagnostics 2022, 12, 3130, doi:10.3390/diagnostics12123130 . . . . . . . . . . . . 367
Martina Sollini, Margarita Kirienko, Noemi Gozzi, Alessandro Bruno, Chiara Torrisi,
Luca Balzarini, Emanuele Voulaz, et al.
The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung
Lesions on CT Scans: Ready for the “Real World”?
Reprinted from: Diagnostics 2023, 15, 357, doi:10.3390/cancers15020357 . . . . . . . . . . . . . . . 385
Jang Yoo, Jaeho Lee, Miju Cheon, Sang-Keun Woo, Myung-Ju Ahn, Hong Ryull Pyo,
Yong Soo Choi, et al.
Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to
Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell
Lung Cancer
Reprinted from: Cancers 2022, 14, 1987, doi:10.3390/cancers14081987 . . . . . . . . . . . . . . . . 399
Gi Hwan Kim, Yong Mee Cho, So-Woon Kim, Ja-Min Park, Sun Young Yoon, Gowun Jeong,
Dong-Myung Shin, et al.
Synaptophysin, CD117, and GATA3 as a Diagnostic Immunohistochemical Panel for Small Cell
Neuroendocrine Carcinoma of the Urinary Tract
Reprinted from: Cancers 2022, 14, 2495, doi:10.3390/cancers14102495 . . . . . . . . . . . . . . . . 411
Yulan Zhao, Ting Huang and Pintong Huang
Integrated Analysis of Tumor Mutation Burden and Immune Infiltrates in
Hepatocellular Carcinoma
Reprinted from: Diagnostics 2022, 12, 1918, doi:10.3390/diagnostics12081918 . . . . . . . . . . . . 425
Qing Li, Ruijie Wang, Zhonglin Xie, Lanbo Zhao, Yiran Wang, Chao Sun, Lu Han, et al.
Clinically Applicable Pathological Diagnosis System for Cell Clumps in Endometrial Cancer
Screening via Deep Convolutional Neural Networks
Reprinted from: Cancers 2022, 14, 4109, doi:10.3390/ cancers14174109 . . . . . . . . . . . . . . . . 443
.Yimin Guo, Ting Lyu, Shuguang Liu, Wei Zhang, Youjian Zhou, Chao Zeng
and Guangming Wu
Learn to Estimate Genetic Mutation and Microsatellite Instability with Histopathology H&E
Slides in Colon Carcinoma
Reprinted from: Cancers 2022, 14, 4144, doi:10.3390/cancers14174144 . . . . . . . . . . . . . . . . 455
vii
Zhengjie Ou,Wei Mao, Lihua Tan, Yanli Yang, Shuanghuan Liu, Yanan Zhang, Bin Li, et al.
Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with
Radical Hysterectomy by Machine Learning
Reprinted from: Curr. Oncol. 2022, 29, 755, doi:10.3390/curroncol29120755 . . . . . . . . . . . . . 471
Qian Yao, Wei Hou, Kaiyuan Wu, Yanhua Bai, Mengping Long, Xinting Diao, Ling Jia, et al.
Using Whole Slide Gray Value Map to Predict HER2 Expression and FISH Status in
Breast Cancer
Reprinted from: Cancers 2022, 14, 6233, doi:10.3390/cancers14246233 . . . . . . . . . . . . . . . . 489
Wei Zhang, Weiting Zhang, Xiang Li, Xiaoming Cao, Guoqiang Yang and Hui Zhang
Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on
a Clinical–Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic
Resonance Images
Reprinted from: Cancers 2023, 15, 86, doi:10.3390/cancers15010086 . . . . . . . . . . . . . . . . . . 503
Marco Rossi, Salvatore M. Aspromonte, Frederick J. Kohlhapp, Jenna H. Newman,
Alex Lemenze, Russell J. Pepe, Samuel M. DeFina, et al.
Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model
Reprinted from: Diagnostics 2022, 12, 958, doi:10.3390/diagnostics12040958 . . . . . . . . . . . . . 519
Ilkka Haapala, Anton Kondratev, Antti Roine, Meri M¨akel¨a, Anton Kontunen,
Markus Karjalainen, Aki Laakso, et al.
Method for the Intraoperative Detection of IDH Mutation in Gliomas with Differential Mobility
Spectrometry
Reprinted from: Curr. Oncol. 2022, 29, 265, doi:10.3390/curroncol29050265 . . . . . . . . . . . . . 531
Joaquim Carreras, Giovanna Roncador and Rifat Hamoudi
Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based
on Immuno-Oncology and Immune Checkpoint Panels
Reprinted from: Cancers 2022, 14, 5318, doi:10.3390/cancers14215318 . . . . . . . . . . . . . . . . 539
Shihori Tanabe, Sabina Quader, Ryuichi Ono, Horacio Cabral, Kazuhiko Aoyagi,
Akihiko Hirose, Edward J. Perkins, et al.
Regulation of Epithelial–Mesenchymal Transition Pathway and Artificial Intelligence-Based
Modeling for Pathway Activity Prediction
Reprinted from: Curr. Oncol. 2023, 3, 2, doi:10.3390/ onco3010002 . . . . . . . . . . . . . . . . . . 585
Ji-Eun Na, Yeong-Chan Lee, Tae-Jun Kim, Hyuk Lee, Hong-Hee Won,
Yang-Won Min, Byung-Hoon Min, et al.
Machine Learning Model to Stratify the Risk of Lymph Node Metastasis for Early Gastric
Cancer: A Single-Center Cohort Study
Reprinted from: Cancers 2022, 14, 1121, doi:10.3390/cancers14051121 . . . . . . . . . . . . . . . . 599
Jeongmin Lee, Bong Joo Kang, Sung Hun Kim and Ga Eun Park
Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound
Based on Characteristics of CAD Marks and False-Positive Marks
Reprinted from: Cancers 2022, 12, 583, doi:10.3390/diagnostics12030583 . . . . . . . . . . . . . . . 611
Sebastian Ziegelmayer, Markus Graf, Marcus Makowski, Joshua Gawlitza and Felix Gassert
Cost-Effectiveness of Artificial Intelligence Support in Computed Tomography-Based Lung
Cancer Screening
Reprinted from: Cancers 2022, 14, 1729, doi:10.3390/cancers14071729 . . . . . . . . . . . . . . . . 623
viii
Nikitha Vobugari, Vikranth Raja, Udhav Sethi, Kejal Gandhi, Kishore Raja
and Salim R. Surani
Advancements in Oncology with Artificial Intelligence—A Review Article
Reprinted from: Cancers 2022, 14, 1349, doi:10.3390/cancers14051349 . . . . . . . . . . . . . . . . 635
ix
