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Query: UMLS:C0019204 (hepatocellular carcinoma)
 71,386 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Between 1997 and 2007, the celioscopic approach was used for 159 (23%) of the 698 hepatic resections performed at Henri Mondor Hospital. The main selection criteria were the location and size of the lesions. Most lesions were located in anterolateral segments (segments 2 to 6) and were smaller than 50 mm. With experience, the team started to perform major hepatectomies for more deep-seated lesions. The patients were 84 women and 75 men. The indications were benign in 65 cases (40%) and malignant in 94 cases (60%). The most frequent benign disorders were symptomatic hepatocyte tumors and tumors of uncertain nature (adenomas and focal nodular hyperplasia in 40 cases). The malignant lesions comprised 60 cases of hepatocellular carcinoma on a cirrhotic liver and 20 metastases of colorectal cancer. The tumors had a diameter of 44 mm (range 4-170 mm). The technique involved 5 trocars, pneumoperitoneum, and parenchymal section with a combination of a harmonic scalpel, an ultrasonic dissector, and automatic staplers. Intermittent pedicular clamping was used if necessary. The specimen was removed in a protective bag. Manual assistance was used in 14 cases (9%), while the other cases involved a pure coelioscopic approach. Major hepatectomy (three or more segments) was performed in 27 cases (17%) and minor resection in 132 cases (83%). There were 17 right hepatectomies, 11 left hepatectomies, 52 left lobectomies, 37 mono- or bisegmentectomies and 43 atypical resections. The overall laparotomic conversion rate was 10% (16 cases). The reasons for conversion were bleeding in 10 cases and inadequate exposure or progression in 6 cases. The mean operating time was 204 minutes. Nine patients (6%) received blood transfusions. There were no postoperative deaths and the morbidity rate was 18%. The mean resection margin for malignant tumors was 14 mm and there were no relapses on the trocar ports. This series demonstrates the feasibility of celioscopic hepatic resection in selected patients. These operations necessitate expertise in hepatic surgery and advanced coelioscopy, as well as sophisticated instrumentation. The advantages are those of minimally invasive surgery, and simpler re-operation for further hepatectomy or transplantation.
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PMID:[Laparoscopic hepatectomy]. 1866 65

Standardized and robust risk stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation. In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (WSI) to build models for predicting the survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n=194) used to develop our algorithms and an independent validation set (TCGA, n=328). WSIs were first divided into small squares ("tiles") and features were extracted with a pretrained convolutional neural network (preprocessing step). The first deep-learning based algorithm ("SCHMOWDER") uses an attention mechanism on tumoral areas annotated by a pathologist while the second ("CHOWDER") does not require human expertise. In the discovery set, c-indexes for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75, respectively. Both models outperformed a composite score incorporating all baseline variables associated with survival. The prognostic value of the models was further validated in the TCGA dataset, and, as observed in the discovery series, both models had a higher discriminatory power than a score combining all baseline variables associated with survival. Pathological review showed that the tumoral areas most predictive of poor survival were characterized by vascular spaces, the macrotrabecular architectural pattern and a lack of immune infiltration. CONCLUSION: This study shows that artificial intelligence can help refine the prediction of HCC prognosis. It highlights the importance of pathologist/machine interactions for the construction of deep-learning algorithms that benefit from expert knowledge and allow a biological understanding of their output.
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PMID:Predicting survival after hepatocellular carcinoma resection using deep-learning on histological slides. 3210 50