Bladder cancer remains a prevalent and important malignant disease in the US. In 2018, an estimated 62,380 men and 18,810 women will be diagnosed with the disease, and another 17,240 individuals are expected to die from it. The treatment of high-grade T1 bladder cancer, representing 30% of non-muscle invasive bladder cancer (NMIBC) cases, continues to be a particularly challenging clinical problem. The five-year recurrence and progression rates of patients with the T1 disease are 42% and 20-40%, respectively. Those with an increased depth of lamina propria invasion or with extensive lamina propria invasion are more than three times more likely to progress than patients with “superficial” invasion and have more than twice the risk of cancer-specific mortality. The treatment decision-making process is further complicated by a 40% risk of clinical under staging and a 5-year cancer specific survival rate of 70%. Although radical surgery (cystectomy) is the standard treatment for high-risk T1 bladder cancer, with a 90% cancer-specific survival at 5 years, 50-60% of patients have post-surgical complications and the risk of mortality is 2-3% in the first 90 days after surgery. Additionally, the recovery is substantial with a known decline in health-related quality of life primarily due to the need for urinary diversion and the risks of sexual and bowel dysfunction. Consequently, clinicians and patients struggle with the choice of conservative bladder-preserving therapies versus radical cystectomy. To overcome these impediments, clinicians need better tools to accurately stage and risk-stratify patients with T1 bladder cancer to direct curative therapies and minimize treatment-related complications.
Identification of bladder layers from tissue biopsies is the first step towards an accurate diagnosis and prognosis of bladder cancer. We present an automated Bladder Image Analysis System (BLIAS) that can recognize urothelium, lamina propria, and muscularis propria from images of H&E-stained slides of bladder biopsies. Furthermore, we present its clinical application to automate risk stratification of T1 bladder cancer patients based on the depth of lamina propria invasion. The method uses multidimensional scaling and transfer learning in conjunction with convolutional neural networks to identify different bladder layers from H&E images of bladder biopsies. The method was trained and tested on eighty whole slide images of bladder cancer biopsies. Our preliminary findings suggest that the proposed method has good agreement with the pathologist in identification of different bladder layers. Additionally, given a set of tumor nuclei within lamina propria, it has the potential to risk stratify T1 bladder cancer by computing the distance from this set to urothelium and muscularis propria. Our results suggest that a pretrained network trained via transfer learning is better in identifying bladder layers than a conventional deep learning paradigm.