Balutkar SD, Bhavthankar SS, Nagoba BS. Letter to the Editor: Potential pitfalls in deep learning-based imaging for spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(7): 120076 [DOI: 10.4329/wjr.120076]
Corresponding Author of This Article
Basavraj S Nagoba, PhD, Professor, Department of Microbiology, Maharashtra Institute of Medical Sciences and Research (Medical College), Vishwanathpuram, Ambajogai Road, Latur 413531, Maharashtra, India. basavraj.nagoba@mimsr.edu.in
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Radiology, Nuclear Medicine & Medical Imaging
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letter
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Balutkar SD, Bhavthankar SS, Nagoba BS. Letter to the Editor: Potential pitfalls in deep learning-based imaging for spontaneous intracerebral hemorrhage. World J Radiol 2026; 18(7): 120076 [DOI: 10.4329/wjr.120076]
World J Radiol. Jul 28, 2026; 18(7): 120076 Published online Jul 28, 2026. doi: 10.4329/wjr.120076
Letter to the Editor: Potential pitfalls in deep learning-based imaging for spontaneous intracerebral hemorrhage
Sachin D Balutkar, Sachin S Bhavthankar, Basavraj S Nagoba
Sachin D Balutkar, Department of Radiology, Maharashtra Institute of Medical Sciences and Research (Medical College), Latur 413512, Maharashtra, India
Sachin S Bhavthankar, Department of Biochemistry, Maharashtra Institute of Medical Sciences and Research, Latur 413512, Maharashtra, India
Basavraj S Nagoba, Department of Microbiology, Maharashtra Institute of Medical Sciences and Research (Medical College), Latur 413531, Maharashtra, India
Author contributions: Balutkar SD and Bhavthankar SS drafted the primary manuscript; Nagoba BS provided conceptual guidance, performed critical revisions, and edited the text; All the authors participated in the final drafting and have reviewed and approved the submitted version.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Basavraj S Nagoba, PhD, Professor, Department of Microbiology, Maharashtra Institute of Medical Sciences and Research (Medical College), Vishwanathpuram, Ambajogai Road, Latur 413531, Maharashtra, India. basavraj.nagoba@mimsr.edu.in
Received: February 24, 2026 Revised: March 9, 2026 Accepted: May 18, 2026 Published online: July 28, 2026 Processing time: 160 Days and 1.8 Hours
Abstract
The study by Yang and Li in the World Journal of Radiology reports strong predictive performance for hematoma enlargement, perihematomal edema (PHE) and hospital mortality using hand crafted quantitative radiomics and deep learning models in patients with spontaneous intracerebral haemorrhage (ICH). While the authors report strong predictive performance using quantitative radiomics and deep learning features, several methodological concerns warrant discussion. By letting the deep learning models based on pretrained Convolutional Neural Network (CNN) to detect the density range of 50-400 Hounsfield units, may falsely detect age related and pathological basal ganglionic calcifications as heterogeneity within the ICH. This may lead to increased false positive rates in the prediction of hematoma enlargement. Similarly, by not restricting the density range between 25-35 Hounsfield units for pretrained CNN to detect PHE, may also add bias by falsely detecting age related micro ischaemic areas and chronic lacunar infarcts in basal ganglionic region as PHE. Further, hand crafted quantitative radiomics by trained clinical radiologists will calculate volume of ICH and PHE more accurately as compared to the CNN models using only three consecutive axial slices centered on the maximum haematomal cross-sectional area, as reported in the original study. Hence, an integrated model consisting of hand crafted quantitative radiomics by trained clinical radiologist with deep learning models based on pre trained CNN with the above-mentioned enhancements and a human touch by a trained clinical radiologist to remove the mentioned confounding factors is highly recommended for better outcomes.
Core Tip: This critique evaluates a study using radiomics and deep learning to predict outcomes in spontaneous intracerebral hemorrhage. While the original study shows promise, it may be limited by confounding calcifications and chronic ischemic lesions being misidentified as hematoma or edema due to broad Hounsfield units thresholds. Furthermore, the use of limited axial slices rather than full volumetric analysis may reduce accuracy. We propose an integrated approach that combines deep learning efficiency with strict Hounsfield units filtering and expert radiological oversight to minimize false positives and improve predictive precision.
