AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump

Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images: ECMTrans-net

Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system. Accurate and efficient analysis of endometrial cancer pathology images is one of the important research components of computer-aided diagnosis. However, endometrial cancer pathology images have challenges such as smaller solid tumors, lesion areas varying in morphology, and difficulty distinguishing solid and nonsolid tumors, which would affect the accuracy of subsequent pathologic analyses. An Endometrial Cancer Multi-class Transformer Network (ECMTrans-net) is therefore proposed herein to improve the segmentation accuracy of endometrial cancer pathology images. An ECM-Attention module can sequentially infer attention maps along three separate dimensions (channel, local spatial, and global spatial) and multiply the attention maps and the input feature map for adaptive feature refinement. This approach may solve the problems of the small size of solid tumors and similar characteristics of solid tumors to nonsolid tumors and further improve the accuracy of segmentation of solid tumors. In addition, an ECM-Transformer module is proposed, which can fuse multi-class feature information and dynamically adjust the receptive field, solving the issue of complex tumor features. Experiments on the Solid Tumor Endometrial Cancer Pathological (ST-ECP) data set found that performance of the ECMTrans-net was superior to state-of-the-art image segmentation methods, and the average values of accuracy, Mean Intersection over Union, precision, and Dice coefficients were 0.952, 0.927, 0.931, and 0.901, respectively.

Postoperative factors predicting outcomes in patients with Perihilar cholangiocarcinoma undergoing curative resection-a 10-year single-center experience

BACKGROUND

Perihilar cholangiocarcinoma (pCCA) has a 5-year overall survival (OS) ranging from 10-40%, following resection. However, prognostic models for postoperative outcomes are limited by long study timespan and variations in work-up. We investigated postoperative outcomes in patients resected for pCCA in a high-volume center with standardized work-up.

METHOD

Patients resected with confirmed pCCA, between 2013 and 2023, were included. Cox-regression investigated association between postoperative factors and OS as well as disease-free survival (DFS).

RESULTS

Totally, 65 patients were resected for pCCA. The 1-, 3- and 5-year OS rates were 86.1%, 56.5% and 32.6% respectively. The 1-, 3- and 5-year DFS rates were 67.7%, 40.0% and 26.8%, respectively. Portal vein embolization (PVE) (HR 4.52 [CI 1.66-12.27], p = 0.003), lymph node metastasis (LNM) (HR 6.37 [CI 2.06-19.67], p = 0.001) and Clavien-Dindo (CD) ≥3a (HR 2.83 [CI 1.43-5.56], p = 0.002) were associated with inferior OS. Clavien-Dindo (CD) ≥3a (HR 2.10 [CI 1.05-4.22], p = 0.03) and T-stage >2 (HR 2.36 [CI 1.01, 5.05], p = 0.04) were associated with inferior and superior DFS, respectively.

CONCLUSION

PVE, T-stage >2, LNM and CD ≥ III were associated with worse prognosis in resected pCCA. Research is needed to improve pre-operative detection of oncological features and patients with risk of major surgical complications.

Applications of artificial intelligence in biliary tract cancers

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.