Author Interviews, Brigham & Women's - Harvard, Cancer Research, JAMA, Lancet, Lung Cancer, Medical Imaging, Technology / 07.09.2022

MedicalResearch.com Interview with: Raymond H. Mak, MD Radiation Oncology Disease Center Leader for Thoracic Oncology Director of Patient Safety and QualityDirector of Clinical Innovation Associate Professor, Harvard Medical School Cancer - Radiation OncologyRadiation Oncology Department of Radiation Oncology Brigham and Women's Hospital MedicalResearch.com: What is the background for this study? What is the algorithm detecting? Response: Lung cancer, the most common cancer worldwide is highly lethal, but can be treated and cured in some cases with radiation therapy.  Nearly half of lung cancer patients will eventually require some form of radiation therapy, but the planning for a course of radiation therapy currently entails manual, time-consuming, and resource-intensive work by highly trained physicians to segment (target) the cancerous tumors in the lungs and adjacent lymph nodes on three-dimensional images (CT scans). Prior studies have shown substantial variation in how expert clinicians delineate these targets, which can negatively impact outcomes and there is a projected shortage of skilled medical staff to perform these tasks worldwide as cancer rates increase. To address this critical gap, our team developed deep learning algorithms that can automatically target lung cancer in the lungs and adjacent lymph nodes from CT scans that are used for radiation therapy planning, and can be deployed in seconds. We trained these artificial intelligence (AI) algorithms using expert-segmented targets from over 700 cases and validated the performance in over 1300 patients in external datasets (including publicly available data from a national trial), benchmarked its performance against expert clinicians, and then further validated the clinical usefulness of the algorithm in human-AI collaboration experiments that measured accuracy, task speed, and end-user satisfaction. (more…)
Author Interviews, Cancer Research, COVID -19 Coronavirus, Lung Cancer, Surgical Research / 21.06.2022

MedicalResearch.com Interview with: Emanuela Taioli, MD, PhD Director, Institute for Translational Epidemiology Professor, Population Health Science and Policy Professor, Thoracic Surgery Icahn School of Medicine at Mount Sinai New York, NY MedicalResearch.com:  What is the background for this study?  Response: NYC experienced a halt on all elective care from March 22 to June 8, 2020, provoking reduced cancer screening rates, and delayed cancer care and treatment. We wanted to quantify the effect of the “pause” on cancer stage at diagnosis using lung cancer as an example of a condition where early diagnosis can dramatically modify survival. (more…)
Author Interviews, Cost of Health Care, JAMA, Lung Cancer, Stanford, USPSTF / 24.10.2021

MedicalResearch.com Interview with: Summer S Han, PhD Assistant Professor Quantitative Sciences Unit Stanford Center for Biomedical Informatics Research (BMIR) Neurosurgery and Medicine Stanford University School of Medicine Stanford, CA 94304  MedicalResearch.com: What is the background for this study? Response: The US Preventive Services Task Force (USPSTF) issued their 2021 recommendation on lung cancer screening lowering the start age from 55 to 50 years and the minimum pack-year criterion from 30 to 20, relative to the 2013 recommendations. Although costs are expected to increase with the expanded screening eligibility, it is unknown if the new guidelines for lung cancer screening are cost-effective. (more…)
AACR, Author Interviews, Cancer Research, Genetic Research, NIH / 23.04.2021

MedicalResearch.com Interview with: Nishanth Ulhas Nair, Ph.D. Affiliation: Staff Scientist at Cancer Data Science Laboratory, Center for Cancer Research National Cancer Institute (NCI), National Institutes of Health (NIH) Bethesda, Maryland, USA. Date: April 22, 2021 Dr. Raffit Hassan and Dr. Eytan Ruppin at the National Cancer Institute (NCI) are the senior authors of this study.  MedicalResearch.com: What is the background for this study? Response: Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. An in-depth knowledge of genetic, transcriptomic and immunogenic events involved in mesothelioma is critical for successful development of prognostics and therapeutic modalities. In this study we aim to address this by exploring a new large scale patient tumor dataset of 122 mesothelioma patients, called NCI mesothelioma patient data, along with their genomic, transcriptomic, and phenotypic information. Unlike previous large-scale studies which have been focused on malignant pleural mesothelioma patients, our dataset contains an approximately equal representation of malignant pleural and peritoneal mesothelioma patients which allows to identify any differences between them. (more…)