Latent Risk Intrahepatic Cholangiocarcinoma Susceptible to Adjuvant Treatment After Resection: A Clinical Deep Learning Approach

Abstract

Background: Artificial Intelligence (AI) frameworks have emerged as a novel approach in medicine. However, information regarding its applicability and effectiveness in a clinical prognostic factor setting remains unclear.Methods: The AI framework was derived from a pooled dataset of intrahepatic cholangiocarcinoma (ICC) patients from three clinical centers (n = 1,421) by applying the TensorFlow deep learning algorithm to Cox-indicated pathologic (four), serologic (six), and etiologic (two) factors; this algorithm was validated using a dataset of ICC patients from an independent clinical center (n = 234). The model was compared to the commonly used staging system (American Joint Committee on Cancer; AJCC) and methodology (Cox regression) by evaluating the brier score (BS), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and area under curve (AUC) values.Results: The framework (BS, 0.17; AUC, 0.78) was found to be more accurate than the AJCC stage (BS, 0.48; AUC, 0.60; IDI, 0.29; NRI, 11.85; P < 0.001) and the Cox model (BS, 0.49; AUC, 0.70; IDI, 0.46; NRI, 46.11; P < 0.001). Furthermore, hazard ratios greater than three were identified in both overall survival (HR; 3.190; 95% confidence interval [CI], 2.150-4.733; P < 0.001) and disease-free survival (HR, 3.559; 95% CI, 2.500-5.067; P < 0.001) between latent risk and stable groups in validation. In addition, the latent risk subgroup was found to be significantly benefited from adjuvant treatment (HR, 0.459; 95% CI, 0.360-0.586; P < 0.001).Jeong et al. Latent Risk Intrahepatic CholangiocarcinomaConclusions: The AI framework seems promising in the prognostic estimation and stratification of susceptible individuals for adjuvant treatment in patients with ICC after resection. Future prospective validations are needed for the framework to be applied in clinical practice.

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

“…Although their so-called EHBH-ICC score outperformed the AJCC 8th and LCSGJ staging systems, the final model’s C-Index was only moderate (0.69 for training and 0.67 for internal validation). The latest attempt by Jeong et al achieved better values: in contrast to the two attempts before, but similar to our study, they used a Tensorflow deep learning algorithm to create a scoring system based on the wide range of four postoperative histopathological, six serological, and two etiological factors [ 20 ]. This system yielded an AUC of 0.78 in the original study and was more accurate than the AJCC staging system (0.60).…”

“…Systems based on ML have proven their feasibility and superiority compared to conventional scoring systems in survival prediction for hepatocellular and colorectal cancer [ 15 , 16 , 17 ]. Thus far, for ICC, a few similar approaches have been tried for the subgroup of resected patients in order to calculate the risk of recurrence, to decide upon adjuvant treatment, and to predict the median overall survival (OS) [ 18 , 19 , 20 ]. For these decisions, such approaches outperformed the conventional scoring systems.…”

Applications of artificial intelligence in pancreatic and biliary diseases

“…used a DL neural network to predict the risk of recurrence after surgical resection of ICC based on 12 parameters. The finding showed that the AI framework performed better compared with the American Joint Committee on Cancer stage and Cox regression analysis (AUC 0.78 vs 0.60 and 0.70) 51 . Springer et al .…”

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

“…The authors concluded that this model could be used to inform presurgical decisions-for example, the use of neoadjuvant therapy for patients with poor prognoses[ 139 ]. In a different study, the researchers developed a DNN model to establish an AI framework through which specific prognostic groups could be used to identify which patients were more likely to benefit from different treatment modalities such as neoadjuvant chemotherapy or transarterial chemoembolization[ 140 ]. The framework was found to be significantly more accurate than the current guidelines of the American Joint Committee of Cancer[ 140 ].…”

“…In a different study, the researchers developed a DNN model to establish an AI framework through which specific prognostic groups could be used to identify which patients were more likely to benefit from different treatment modalities such as neoadjuvant chemotherapy or transarterial chemoembolization[ 140 ]. The framework was found to be significantly more accurate than the current guidelines of the American Joint Committee of Cancer[ 140 ]. Finally, a study developed an ANN to predict which patients with inoperable hilar CCA will develop early occlusion following a bilateral plastic stent placement[ 141 ].…”