A Generalized Tumor Segmentation Algorithm for Varying Breast Cancer Subtypes
DescriptionBackground. Automated breast tumor segmentation for dynamic contrast-enhanced magnetic resonance (DCE-MR) is a crucial step to advance and help with the implementation of radiomics for image-based, quantitative assessment of breast tumors and cancer phenotyping. Current studies focus on developing tumor segmentation, which often requires initial seed points from expert radiologists or atlas-based segmentation methods. We develop a robust, fully automated end-to-end segmentation pipeline for breast cancers on bilateral breast MR studies.
Methods. On IRB-approved diverse breast cancer MR cases, a deep learning segmentation algorithm was created and trained. The model’s backbone is UNet++, which consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the skip connections and all the constituent UNets are trained simultaneously to learn a shared image representation. This design not only improves the overall segmentation performance, but also enables model pruning during the inference time. The model was trained on the breast tumors located independently by a radiologist with consensus review by a second radiologist with at least five years of experience. MRI was performed using a 3.0-T imaging system in the prone position with a dedicated 16-channel breast coil and T1 weighted DEC-MR images were analyzed for the study. We used 80:20 random split for training and validation of the model.
Results. A total of 124 breast cancer patients had pre-treatment MR imaging before the start of NST - the cohort comprised 49 HR+HER2-, 37 HR+HER2+, 11 HR-HER2+, and 27 TNBC cases (mean tumor 2.3 cm (+/- 3.1mm).) The model was tested on 2571 individual images. Overall, the model scored 0.85 [0.84 – 0.86, 95% CI] dice score and 0.8[0.79-0.81, 95% CI] IoU score. TNBC tumors scored dice [0.88 – 0.89, 95% CI], HER2 neg and ER/PR positive dice [0.84-0.85, 95% CI] and HER2 positive dice [0.84-0.85, 95% CI]. We observed that model performed equally for the solid tumors and irregular shapes and didn’t observe any difference in the segmentation performance between residual and non-residual tumors types - dice score [0.85 – 0.86, 95% CI] and [0.83 – 0.84, 95% CI] respectively.
Conclusion. The proposed segmentation model can perform equally well on various clinical breast cancer subtypes. The model has high false positive rate towards biopsy clip and high background enhancement which we plan to solve by adding annotation of the clip and high non-cancer enhancement in future training data. We will release the trained model with open-source license to increase the scalability of the radiomics studies with fully automated segmentation. Given the importance of breast cancer subtypes as prognostic factors in women with operable breast cancer, automated segmentation of varying breast tumor subtypes will help to analyze imaging biomarkers embedded within the standard of care imaging studies in a larger scale study which will ¬potentially help radiologists, pathologists, surgeons, and clinicians understand features driving breast cancer phenotypes and pave the way for developing digital twin for breast cancer patients.
Methods. On IRB-approved diverse breast cancer MR cases, a deep learning segmentation algorithm was created and trained. The model’s backbone is UNet++, which consists of U-Nets of varying depths whose decoders are densely connected at the same resolution via the skip connections and all the constituent UNets are trained simultaneously to learn a shared image representation. This design not only improves the overall segmentation performance, but also enables model pruning during the inference time. The model was trained on the breast tumors located independently by a radiologist with consensus review by a second radiologist with at least five years of experience. MRI was performed using a 3.0-T imaging system in the prone position with a dedicated 16-channel breast coil and T1 weighted DEC-MR images were analyzed for the study. We used 80:20 random split for training and validation of the model.
Results. A total of 124 breast cancer patients had pre-treatment MR imaging before the start of NST - the cohort comprised 49 HR+HER2-, 37 HR+HER2+, 11 HR-HER2+, and 27 TNBC cases (mean tumor 2.3 cm (+/- 3.1mm).) The model was tested on 2571 individual images. Overall, the model scored 0.85 [0.84 – 0.86, 95% CI] dice score and 0.8[0.79-0.81, 95% CI] IoU score. TNBC tumors scored dice [0.88 – 0.89, 95% CI], HER2 neg and ER/PR positive dice [0.84-0.85, 95% CI] and HER2 positive dice [0.84-0.85, 95% CI]. We observed that model performed equally for the solid tumors and irregular shapes and didn’t observe any difference in the segmentation performance between residual and non-residual tumors types - dice score [0.85 – 0.86, 95% CI] and [0.83 – 0.84, 95% CI] respectively.
Conclusion. The proposed segmentation model can perform equally well on various clinical breast cancer subtypes. The model has high false positive rate towards biopsy clip and high background enhancement which we plan to solve by adding annotation of the clip and high non-cancer enhancement in future training data. We will release the trained model with open-source license to increase the scalability of the radiomics studies with fully automated segmentation. Given the importance of breast cancer subtypes as prognostic factors in women with operable breast cancer, automated segmentation of varying breast tumor subtypes will help to analyze imaging biomarkers embedded within the standard of care imaging studies in a larger scale study which will ¬potentially help radiologists, pathologists, surgeons, and clinicians understand features driving breast cancer phenotypes and pave the way for developing digital twin for breast cancer patients.
Event Type
Workshop
TimeSunday, 13 November 20229:45am - 10am CST
LocationD222
W
Recorded