scholarly journals CT radiomics nomogram for the preoperative prediction of severe post-hepatectomy liver failure in patients with huge (≥ 10 cm) hepatocellular carcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fei Xiang ◽  
Xiaoyuan Liang ◽  
Lili Yang ◽  
Xingyu Liu ◽  
Sheng Yan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Decision Tree ◽  
Liver Failure ◽  
Meld Score ◽  
Training Dataset ◽  
Decision Tree Analysis ◽  
Multivariable Logistic Regression Model ◽  
Tree Analysis ◽  
Risk Patients ◽  
Radiomics Signature

Abstract Background This study aimed to establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥ 10 cm) hepatocellular carcinoma (HCC). Methods One hundred eighty-six patients with huge HCC (training dataset, n = 131 and test dataset, n = 55) that underwent curative hepatic resection were included in this study. The least absolute shrinkage and selection operator (LASSO) approach was applied to develop a radiomics signature for grade B or C PHLF prediction using the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. Decision tree analysis was performed to stratify the risk for severe PHLF. Results The radiomics signature consisting of nine features predicted severe PHLF with AUCs of 0.766 and 0.745 for the training and test datasets. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and the model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination ability, with AUCs of 0.842 and 0.863 for the training and test datasets, respectively. Based on decision tree analysis, patients were divided into three risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or radiomics score ≥ − 0.247 but underwent partial resections; intermediate-risk patients with radiomics score < − 0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ − 0.247 and underwent extended resections. Conclusions The radiomics nomogram could predict severe PHLF in huge HCC patients. A decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

scholarly journals CT Radiomics Nomogram for the Preoperative Prediction of Severe Post-Hepatectomy Liver Failure in Patients with Huge (≥10 cm) Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Fei Xiang ◽  
Xiaoyuan Liang ◽  
Lili Yang ◽  
Xingyu Liu ◽  
Sheng Yan
Keyword(s):  
Hepatocellular Carcinoma ◽  
Decision Tree ◽  
Liver Failure ◽  
Extent Of Resection ◽  
Meld Score ◽  
Training Dataset ◽  
Clinical Predictors ◽  
Multivariable Logistic Regression Model ◽  
Risk Patients ◽  
Radiomics Signature

Abstract Background To establish a radiomics-based nomogram for predicting severe (grade B or C) post-hepatectomy liver failure (PHLF) in patients with huge (≥10 cm) hepatocellular carcinoma (HCC).Methods 186 patients with huge HCC (n = 131 for training dataset and n = 55 for test dataset) who underwent curative hepatic resection were included. The least absolute shrinkage and selection operator approach was applied to develop the radiomics signature for grade B or C PHLF prediction in the training dataset. A multivariable logistic regression model was used by incorporating radiomics signature and other clinical predictors to establish a radiomics nomogram. A decision tree was created to stratify the risk for severe PHLF.Results The radiomics signature consisting of nine features predicted severe PHLF with an AUC of 0.766 and 0.745 in the training and test datasets, respectively. The radiomics nomogram was generated by integrating the radiomics signature, the extent of resection and model for end-stage liver disease (MELD) score. The nomogram exhibited satisfactory discrimination and calibration, with an AUC of 0.842 and 0.863 in the training and test datasets, respectively. Decision tree split patients into 3 risk classes: low-risk patients with radiomics score < -0.247 and MELD score < 10 or，radiomics score ≥ -0.247 and underwent partial resections; intermediate-risk patients with radiomics score < -0.247 but MELD score ≥10; high-risk patients with radiomics score ≥ -0.247 and underwent extended resections.Conclusions The radiomics nomogram was able to predict severe PHLF in huge HCC patients. Decision tree may be useful in surgical decision-making for huge HCC hepatectomy.

scholarly journals Alternating Lenvatinib and Trans-Arterial Therapy Prolongs Overall Survival in Patients with Inter-Mediate Stage HepatoCellular Carcinoma: A Propensity Score Matching Study

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 160
Author(s):  
Shigeo Shimose ◽  
Hideki Iwamoto ◽  
Masatoshi Tanaka ◽  
Takashi Niizeki ◽  
Tomotake Shirono ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Propensity Score ◽  
Decision Tree ◽  
Propensity Score Matching ◽  
Intermediate Stage ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Factors Associated ◽  
The Impact

We aimed to evaluate the impact of alternating lenvatinib (LEN) and trans-arterial therapy (AT) in patients with intermediate-stage hepatocellular carcinoma (HCC) after propensity score matching (PSM). This retrospective study enrolled 113 patients with intermediate-stage HCC treated LEN. Patients were classified into the AT (n = 41) or non-AT group (n = 72) according to the post LEN treatment. Overall survival (OS) was calculated using the Kaplan–Meier method and analyzed using a log-rank test after PSM. Factors associated with AT were evaluated using a decision tree analysis. After PSM, there were no significant differences in age, sex, etiology, or albumin-bilirubin (ALBI) score/grade between groups. The survival rate of the AT group was significantly higher than that of the non-AT group (median survival time; not reached vs. 16.3 months, P = 0.01). Independent factors associated with OS were AT and ALBI grade 1 in the Cox regression analysis. In the decision tree analysis, age and ALBI were the first and second splitting variables for AT. In this study, we show that AT may improve prognosis in patients with intermediate-stage HCC. Moreover, alternating LEN and trans-arterial therapy may be recommended for patients below 70 years of age with ALBI grade 1.

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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