Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS: A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS: Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS: AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.

Effect of telemedicine use on medical spending and health care utilization among patients with gastrointestinal cancer.

BACKGROUND: We sought to assess the impact of telemedicine on healthcare utilization and medical expenditures among patients with a diagnosis of gastrointestinal (GI) cancer. METHODS: Patients with newly diagnosed GI cancer from 2013 to 2020 were identified from the IBM MarketScan database (IBM Watson Health) . Healthcare utilization, total medical outpatient insurance payments within 1 year post-diagnosis, and out-of-pocket (OOP) expenses among telemedicine users and non-users were assessed after propensity score matching (PSM). RESULTS: Among the 32,677 patients with GI cancer (esophageal, n = 1862, 5.7%; gastric, n = 2009, 6.1%; liver, n = 2929, 9.0%; bile duct, n = 597, 1.8%; pancreas, n = 3083, 9.4%; colorectal, n = 22,197, 67.9%), a total of 3063 (9.7%) utilized telemedicine. After PSM (telemedicine users, n = 3064; non-users, n = 3064), telemedicine users demonstrated a higher frequency of clinic visits (median: 5.0 days, IQR 4.0-7.0 vs non-users: 2.0 days, IQR 2.0-3.0, P < .001) and fewer potential days missed from daily activities (median: 7.5 days, IQR 4.5-12.5 vs non-users: 8.5 days, IQR 5.5-13.5, P < .001). Total medical spending per month and utilization of emergency room (ER) visits for telemedicine users were higher vs non-users (median: $10,658, IQR $5112-$18,528 vs non-users: $10,103, IQR $4628-$16,750; 46.8% vs 42.6%, both P < .01), whereas monthly OOP costs were comparable (median: $273, IQR $137-$449 for telemedicine users vs non-users: $268, IQR $142-$434, P = .625). CONCLUSION: Telemedicine utilization was associated with increased outpatient clinic visits yet reduced potential days missed from daily activities among patients with GI cancer. Telemedicine users tended to have more ER visits and total medical spending per month, although monthly OOP costs were comparable with non-users.