Artificial intelligence in cancer diagnosis and therapy / Hamid Khayyam, [and three others], [editors].

Format:

Book

Contributor:

Khayyam, Hamid, editor.

Language:

English

Subjects (All):

Artificial intelligence.

Physical Description:

1 online resource

Place of Publication:

Basel : MDPI, [2023]

Summary:

This reprint covers some significant impacts in the recent research in both the private and public sectors of cancer diagnosis and therapy, in which Artificial Intelligence (AI) and Machine Learning are significant. This reprint is also a collection of forty different complex and challenging problems arranged in five groups: AI in prognosis, grading, and prediction, AI in clinical image analysis, AI models for pathological diagnosis, ML and statistical models for molecular cancer diagnostics and genetics, and AI in triage, risk stratification, and screening cancer, which are all focused on using AI in cancer diagnosis and therapy. All the necessary concepts, solutions, methodologies, and references are supplied except for some fundamental knowledge that is well-known in the general fields of AI and cancer diagnosis and therapy. The readers may, therefore, gain the main concepts of each chapter, with as little of a need as possible to refer to the concepts of the other chapters and references. The readers may hence start to read one or more chapters of the book for their own interests.

Contents:

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

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 Sanguesa ¨ Nebot, Blanca Mart´ınez

de las Heras, Ulrike Potschger, ¨ 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

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, Jiangfeng Wu, Yunlai Wang 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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-0365-6673-2