Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection

• Published in: International journal for computer assisted radiology and surgery 2022, Vol.17 (1), p.121-128
• Main Authors: Javadi, Golara, Bayat, Sharareh, Kazemi Esfeh, Mohammad Mahdi, Samadi, Samareh, Sedghi, Alireza, Sojoudi, Samira, Hurtado, Antonio, Chang, Silvia, Black, Peter, Mousavi, Parvin, Abolmaesumi, Purang
• Format: Article
• Language: English
• Subjects: Biopsy; Clinical decision making; Computer Imaging; Computer Science; Decision making; Health Informatics; Humans; Image-Guided Biopsy; Imaging; Machine learning; Magnetic Resonance Imaging; Male; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Prostate - diagnostic imaging; Prostate cancer; Prostatic Neoplasms - diagnostic imaging; Radiology; Surgery; Testing time; Ultrasonic imaging; Ultrasonography, Interventional; Uncertainty; Vision
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-021-02485-z

Purpose Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy. Methods We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure. Results Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively. Conclusion Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield.