Application of Hazard Function to Investigate Recurrence of Intrahepatic Cholangiocarcinoma After Curative-Intent Liver Resection: A Novel Approach to Characterize Recurrence

Development and Validation of a Machine-Learning Model to Predict Early Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

The high incidence of early recurrence after hepatectomy for intrahepatic cholangiocarcinoma (ICC) has a detrimental effect on overall survival (OS). Machine-learning models may improve the accuracy of outcome prediction for malignancies.

METHODS

Patients who underwent curative-intent hepatectomy for ICC were identified using an international database. Three machine-learning models were trained to predict early recurrence (< 12 months after hepatectomy) using 14 clinicopathologic characteristics. The area under the receiver operating curve (AUC) was used to assess their discrimination ability.

RESULTS

In this study, 536 patients were randomly assigned to training (n = 376, 70.1%) and testing (n = 160, 29.9%) cohorts. Overall, 270 (50.4%) patients experienced early recurrence (training: n = 150 [50.3%] vs testing: n = 81 [50.6%]), with a median tumor burden score (TBS) of 5.6 (training: 5.8 [interquartile range {IQR}, 4.1-8.1] vs testing: 5.5 [IQR, 3.7-7.9]) and metastatic/undetermined nodes (N1/NX) in the majority of the patients (training: n = 282 [75.0%] vs testing n = 118 [73.8%]). Among the three different machine-learning algorithms, random forest (RF) demonstrated the highest discrimination in the training/testing cohorts (RF [AUC, 0.904/0.779] vs support vector machine [AUC, 0.671/0.746] vs logistic regression [AUC, 0.668/0.745]). The five most influential variables in the final model were TBS, perineural invasion, microvascular invasion, CA 19-9 lower than 200 U/mL, and N1/NX disease. The RF model successfully stratified OS relative to the risk of early recurrence.

CONCLUSIONS

Machine-learning prediction of early recurrence after ICC resection may inform tailored counseling, treatment, and recommendations. An easy-to-use calculator based on the RF model was developed and made available online.

Impact of Staging Concordance and Downstaging After Neoadjuvant Therapy on Survival Following Resection of Intrahepatic Cholangiocarcinoma: A Bayesian Analysis

INTRODUCTION

Concordance between clinical and pathological staging, as well as the overall survival (OS) benefit associated with neoadjuvant therapy (NAT) remain ill-defined. We sought to determine the impact of staging accuracy and NAT downstaging on OS among patients with intrahepatic cholangiocarcinoma (ICC).

METHODS

Patients treated for ICC between 2010 and 2018 were identified using the National Cancer Database. A Bayesian approach was applied to estimate NAT downstaging. OS was assessed relative to staging concordant/overstaged disease treated with upfront surgery, understaged disease treated with upfront surgery, no downstaging, and downstaging after NAT.

RESULTS

Among 3384 patients, 2904 (85.8%) underwent upfront surgery, whereas 480 (14.2%) received NAT and 85/480 (18.4%) were downstaged. Patients with cT3 (odds ratio [OR] 2.12, 95% confidence interval [CI] 1.34-3.34), cN1 (OR 2.47, 95% CI 1.71-3.58) disease, and patients treated at high-volume facilities (OR 1.63, 95% CI 1.13-2.36) were more likely to receive NAT (all p < 0.05). Median OS was 40.1 months (95% CI 38.6-43.4). Patients with cT1-2N1 (NAT: 31.5 months vs. upfront surgery: 22.4 months; p = 0.04) and cT3-4N1 (NAT: 27.8 months vs. upfront surgery: 14.4 months; p = 0.01) disease benefited most from NAT. NAT downstaging decreased the risk of death among patients with cT3-4N1 disease (hazard ratio [HR] 0.35, 95% CI 0.15-0.82). In contrast, understaged patients with cT1-2N0/X (HR 2.15, 95% CI 1.83-2.53) and cT3-4N0/X (HR 1.71, 95% CI 1.06-2.74) disease treated with upfront surgery had increased risk of death.

CONCLUSIONS

Patients with N1 ICC treated with NAT demonstrated improved OS compared with upfront surgery. Downstaging secondary to NAT conferred survival benefits among patients with cT3-4N1 versus upfront surgery. NAT should be considered in ICC patients with advanced T disease and/or nodal metastases.

The Application of Artificial Intelligence to Investigate Long-Term Outcomes and Assess Optimal Margin Width in Hepatectomy for Intrahepatic Cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is associated with poor long-term outcomes, and limited evidence exists on optimal resection margin width. This study used artificial intelligence to investigate long-term outcomes and optimal margin width in hepatectomy for ICC.

METHODS

The study enrolled patients who underwent curative-intent resection for ICC between 1990 and 2020. The optimal survival tree (OST) was used to investigate overall (OS) and recurrence-free survival (RFS). An optimal policy tree (OPT) assigned treatment recommendations based on random forest (RF) counterfactual survival probabilities associated with each possible margin width between 0 and 20 mm.

RESULTS

Among 600 patients, the median resection margin was 4 mm (interquartile range [IQR], 2-10). Overall, 379 (63.2 %) patients experienced recurrence with a 5-year RFS of 28.3 % and a 5-year OS of 38.7 %. The OST identified five subgroups of patients with different OS rates based on tumor size, a carbohydrate antigen 19-9 [CA19-9] level higher than 200 U/mL, nodal status, margin width, and age (area under the curve [AUC]: training, 0.81; testing, 0.69). The patients with tumors smaller than 4.8 cm and a margin width of 2.5 mm or greater had a relative increase in 5-year OS of 37 % compared with the entire cohort. The OST for RFS estimated a 46 % improvement in the 5-year RFS for the patients younger than 60 years who had small (<4.8 cm) well- or moderately differentiated tumors without microvascular invasion. The OPT suggested five optimal margin widths to maximize the 5-year OS for the subgroups of patients based on age, tumor size, extent of hepatectomy, and CA19-9 levels.

CONCLUSIONS

Artificial intelligence OST identified subgroups within ICC relative to long-term outcomes. Although tumor biology dictated prognosis, the OPT suggested that different margin widths based on patient and disease characteristics may optimize ICC long-term survival.