Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

BACKGROUND: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. METHODS: We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. RESULTS: The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. CONCLUSIONS: We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds.

Systemic Therapy for Hepatocellular Carcinoma: Advances and Hopes.

The majority of patients with hepatocellular carcinoma (HCC) are diagnosed at an advanced stage that can only benefit from systemic treatments. Although HCC is highly treatmentresistant, significant achievements have been made in the molecular targeted therapy and immunotherapy of HCC. In addition to regorafenib, cabozantinib and ramucirumab were approved for the second- line targeted treatment by the FDA after disease progression on sorafenib. Nivolumab failed to demonstrate remarkable benefit in overall survival (OS) as first-line therapy, while pembrolizumab did not achieve pre-specified statistical significance in both OS and progression-free survival (PFS) as second-line treatment. Combinations of targeted agents, immune checkpoint inhibitors and other interventions showed favorable results. In this review, we summarized the progress of systemic therapy in HCC and discussed the future directions of the treatment of HCC.