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Digital Health Portals for Individuals Living With or Beyond Cancer: Patient-Driven Scoping Review

Digital Health Portals for Individuals Living With or Beyond Cancer: Patient-Driven Scoping Review

The complexity of oncology care, involving multidisciplinary teams, intensive treatments, and frequent clinical interactions, highlights the need for effective information management and communication systems. Patient portal can improve communication in complex context by promoting informational continuity, enhancing care coordination, and supporting engagement among individuals living with or beyond cancer [1-8].

Steven Ouellet, Florian Naye, Wilfried Supper, Chloé Cachinho, Marie-Pierre Gagnon, Annie LeBlanc, Marie-Claude Laferrière, Simon Décary, Maxime Sasseville

JMIR Cancer 2025;11:e72862

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong

Previous studies have successfully applied these methods to identify clusters of symptoms [28], clinical prognostic features in oncology [29], and comorbidities with other chronic diseases [30]. In the second step, the study incorporated domain knowledge into the interpretation of the clusters [31,32].

Chun Sing Lam, Rong Hua, Herbert Ho-Fung Loong, Chun-Kit Ngan, Yin Ting Cheung

JMIR Cancer 2025;11:e71937

Examining How Technology Supports Shared Decision-Making in Oncology Consultations: Qualitative Thematic Analysis

Examining How Technology Supports Shared Decision-Making in Oncology Consultations: Qualitative Thematic Analysis

This paper examines how EHRs and other digital tools are used in practice to inform possible future improvements in applied digital technology to facilitate SDM in oncology consultations. Hence, the objective of this study was to explore how health care professionals use digital technology to support SDM in oncology consultations, understand the barriers to technology that support SDM in oncology consultations, and understand the opportunities for future technology to improve SDM in oncology consultations.

Alan Yung, Tim Shaw, Judy Kay, Anna Janssen

JMIR Cancer 2025;11:e70827

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

Next-Generation Sequencing–Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non–Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods

These variables included patient age at advanced or metastatic diagnosis date (years), sex (male or female), race (Asian, Black, White, and other), insurance type (public, private, or other), Eastern Cooperative Oncology Group (ECOG) performance status (0-4), smoking history (ever vs never smoker), body weight (kilograms), BMI (kg/m2), practice setting (academic or community), practice volume (the average number of those with NSCLC receiving care at the site where the included patient received care by index

Alan James Michael Brnabic, Ilya Lipkovich, Zbigniew Kadziola, Dan He, Peter M Krein, Lisa M Hess

JMIR Cancer 2025;11:e64399

Internet-Based Cognitive Behavioral Therapy Interventions for Caregivers of Patients With Cancer: Scoping Review

Internet-Based Cognitive Behavioral Therapy Interventions for Caregivers of Patients With Cancer: Scoping Review

The English search terms were “neoplas*, carcinoma*, tumor, oncology, cancer*;” “Cognitive Behavio*, Behavio* Therap*, Cognitive Therap*, ICBT, cognitive behavioural therapy, CCBT;” “online, network, Internet, smartphone, telephone, computer;” and “caregiver*, spouse, family, informal caregiver, couple*.” The search strategy for each database was documented in the Multimedia Appendix 1. The eligibility criteria is presented in Textbox 1.

Chun Tong Shen, Jian Shi, Feng Xia Liu, Xiao Meng Lu

JMIR Cancer 2025;11:e67131

Piloting the Extension for Community Healthcare Outcomes (ECHO) Pediatric Oncology Telehealth Education Program in Western Kenya: Implementation Study

Piloting the Extension for Community Healthcare Outcomes (ECHO) Pediatric Oncology Telehealth Education Program in Western Kenya: Implementation Study

Since then, AMPATH (Academic Model Providing Access To Health care) has expanded to include multiple North American institutions and a broader focus of acute and chronic diseases, including both pediatric and adult oncology [7]. Pediatric oncology training was further bolstered through partnerships with Princess Máxima Center in the Netherlands and Dutch pediatric oncology colleagues.

Tyler Severance, Gilbert Olbara, Festus Njuguna, Martha Kipng'etich, Sandra Lang'at, Maureen Kugo, Jesse Lemmen, Marjorie Treff, Patrick Loehrer, Terry Vik

JMIR Form Res 2025;9:e59776

Multidisciplinary Oncology Education Among Postgraduate Trainees: Systematic Review

Multidisciplinary Oncology Education Among Postgraduate Trainees: Systematic Review

The objective of this study was to perform a systematic review of the literature to evaluate the multidisciplinary oncology education in postgraduate medical training (ie, interns, residents, and fellows). This study provides a review of literature analyzing the education of learners about the role of any collaborating physician specialty involved in oncology care, including but not limited to, medical oncology, radiation oncology, surgical oncology, and palliative care.

Houman Tahmasebi, Gary Ko, Christine M Lam, Idil Bilgen, Zachary Freeman, Rhea Varghese, Emma Reel, Marina Englesakis, Tulin D Cil

JMIR Med Educ 2025;11:e63655

Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis

Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis

We included the following characteristics for patients diagnosed with early-stage breast cancer: age; Eastern Cooperative Oncology Group performance status; BMI; comorbidities such as diabetes, heart failure, coronary artery disease, chronic obstructive pulmonary disease, and cognitive impairments; history of hospitalizations; and polypharmacy. We also gathered biological indicators at the time of diagnosis, which included hemoglobin levels, lymphocyte counts, and creatinine clearance.

Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

JMIR Cancer 2025;11:e64000

Application of AI Chatbot in Responding to Asynchronous Text-Based Messages From Patients With Cancer: Comparative Study

Application of AI Chatbot in Responding to Asynchronous Text-Based Messages From Patients With Cancer: Comparative Study

The research team initially identified and sent invitation emails to 60 oncologists who were members of the Chinese Anti-Cancer Association, a leading oncology academic society in China. While oncologists were located across different regions, there were no restrictions based on years of experience or subspecialization. The email requested their participation in the study and asked them to provide real-world, asynchronous, text-based medical records.

Xuexue Bai, Shiyong Wang, Yuanli Zhao, Ming Feng, Wenbin Ma, Xiaomin Liu

J Med Internet Res 2025;27:e67462

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

A Deep Learning–Enabled Workflow to Estimate Real-World Progression-Free Survival in Patients With Metastatic Breast Cancer: Study Using Deidentified Electronic Health Records

Hormone receptor status, human epidermal growth factor receptor-2 (HER-2) status, and Eastern Cooperative Oncology Group scores were captured from clinical notes using the clinical NLP engine. A rule-based approach was used to identify the initiation date of first-line therapy in m BC by analyzing drug orders and administration records. To ensure reliability, only orders appearing for the first time after metastasis diagnosis were included.

Gowtham Varma, Rohit Kumar Yenukoti, Praveen Kumar M, Bandlamudi Sai Ashrit, K Purushotham, C Subash, Sunil Kumar Ravi, Verghese Kurien, Avinash Aman, Mithun Manoharan, Shashank Jaiswal, Akash Anand, Rakesh Barve, Viswanathan Thiagarajan, Patrick Lenehan, Scott A Soefje, Venky Soundararajan

JMIR Cancer 2025;11:e64697