Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images
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Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading system. It was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the observers (o1, o2 and o3) (κ_o1,dl=0.81, κ_o2,dl=0.53, κ_o3,dl=0.40) than the observers amongst each other (κ_o1,o2=0.58, κ_o1,o3=0.50, κ_o2,o3=0.42) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers (κ_o1,dl=0.77, κ_o2,dl=0.75, κ_o3,dl=0.70) as the observers amongst each other (κ_o1,o2=0.77, κ_o1,o3=0.75, κ_o2,o3=0.72). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade.
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