Credit: AI Trends
2. Segmenting tumors
Analyzing tumor volume is the step in cancer treatment that immediately follows diagnosis, but traditional methods used by radiologists—namely response evaluation criteria in solid tumors, or RECIST—are slow and can be off by almost 50%.
Scientists have used CNNs to segment brain tumors, liver tumors and optic path gliomas to a greater degree of accuracy. In the liver cancer study, a team used CNNs for the segmentation of liver tumors in follow-up CTs, inputing a baseline CT scan, delineating of the CT scan and follow-up scan into the CNN to achieve automated segmentation.
“A major advantage of CNNs over semiautomatic methods is that the need to customize handcrafted features is obviated because of their ability to automatically identify features,” Londhe and Bhasin wrote.
3. Applying precision histology
The authors said histomorphology has been “revolutionized” by precision histology, a type of deep learning. While pathology and diagnostics have relied for years on the accurate interpretation of H&E-stained slides—interpretation that can be slow and unreliable—deep neural networks (DNNs) are applying algorithms that can speed up the process. DNNs have already been used to analyze skin lesions with a similar accuracy to practicing dermatologists, deconstructing images into pixels and aggregating them to form reproducible characteristics that yield some kind of diagnostic pattern.
“It is likely that DNNs will soon be capable of more accurate analyses based on H&E slides owing to the developments in high-throughput whole-slide scanning technologies,” Londhe and Bhasin said. “This will also lead to the development of a new biological data pool, which will further aid precision oncology.”