Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study

Tao, Xuetong; Gandomkar, Ziba; Li, Tong; Brennan, Patrick C.; Reed, Warren

doi:10.3390/jpm13060888

Cited by 4 publications

(1 citation statement)

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“…In this study, annotations with a RANZCR rating of 1 or 2 were considered 'negative', while those with a rating of 3 and above were classified as 'positive' diagnoses. Further details about the reading procedures have been described in previous works [9][10][11].…”

Section: Data Acquisitionmentioning

confidence: 99%

CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training

Tao,

Gandomkar,

et al. 2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

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Early detection of breast cancer through screening mammography is crucial. However, the interpretation of mammograms is prone to high error rates, and radiologists often exhibit common errors specific to their practice regions. It is essential to identify prevalent errors and offer tailored mammography training to address these region-specific challenges. This study investigated the feasibility of leveraging Convolutional Neural Networks (CNN) with transfer learning to identify areas in screening mammograms that may contribute to a high proportion of false positive diagnoses by radiologists from the same geographical region. We collected mammography test sets evaluated by a cohort of Australian radiologists and segmented error-related patches based on their assessments. Each patch was labeled as "easy" or "difficult", and subsequently, we proposed a patch-wise ResNet model to predict the difficulty level of each patch. Specifically, we employed the pre-trained ResNet-18, ResNet-50, and ResNet-101 as feature extractors. During training, we modified and fine-tuned the fully connected layers for our target task while keeping the convolutional layers frozen. The model's performance was evaluated using 10-fold cross-validation, and the transferred ResNet-50 obtained the highest performance, achieving Receiver Operating Characteristics Area Under the Curve (AUC) values of 0.975 (±0.011) on the validation sets. In conclusion, our study demonstrated the feasibility of employing CNN-based transfer learning to identify the prevalent errors in specific radiology communities. This approach shows promise in automating the customization of mammography training materials to mitigate errors among radiologists in a region.

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Section: Data Acquisitionmentioning

confidence: 99%

CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training

Tao,

Gandomkar,

et al. 2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

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Unbiased employee performance evaluation using machine learning

Nayem,

Uddin

2024

Journal of Open Innovation: Technology, Market, and Complexity

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AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions

Jiang,

Gandomkar,

Trieu

et al. 2024

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Although the value of adding AI as a surrogate second reader in various scenarios has been investigated, it is unknown whether implementing an AI tool within double reading practice would capture additional subtle cancers missed by both radiologists who independently assessed the mammograms. This paper assesses the effectiveness of two state-of-the-art Artificial Intelligence (AI) models in detecting retrospectively-identified missed cancers within a screening program employing double reading practices. The study also explores the agreement between AI and radiologists in locating the lesions, considering various levels of concordance among the radiologists in locating the lesions. The Globally-aware Multiple Instance Classifier (GMIC) and Global–Local Activation Maps (GLAM) models were fine-tuned for our dataset. We evaluated the sensitivity of both models on missed cancers retrospectively identified by a panel of three radiologists who reviewed prior examinations of 729 cancer cases detected in a screening program with double reading practice. Two of these experts annotated the lesions, and based on their concordance levels, cases were categorized as 'almost perfect,' 'substantial,' 'moderate,' and 'poor.' We employed Similarity or Histogram Intersection (SIM) and Kullback–Leibler Divergence (KLD) metrics to compare saliency maps of malignant cases from the AI model with annotations from radiologists in each category. In total, 24.82% of cancers were labeled as “missed.” The performance of GMIC and GLAM on the missed cancer cases was 82.98% and 79.79%, respectively, while for the true screen-detected cancers, the performances were 89.54% and 87.25%, respectively (p-values for the difference in sensitivity < 0.05). As anticipated, SIM and KLD from saliency maps were best in ‘almost perfect,’ followed by ‘substantial,’ ‘moderate,’ and ‘poor.’ Both GMIC and GLAM (p-values < 0.05) exhibited greater sensitivity at higher concordance. Even in a screening program with independent double reading, adding AI could potentially identify missed cancers. However, the challenging-to-locate lesions for radiologists impose a similar challenge for AI.

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Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study

Cited by 4 publications

References 45 publications

CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training

CNN-based transfer learning with 10-fold cross-validation: a novel approach for customized education of mammography training

Unbiased employee performance evaluation using machine learning

AI for interpreting screening mammograms: implications for missed cancer in double reading practices and challenging-to-locate lesions

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