Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection

Advances and current research status of early diagnosis for gallbladder cancer

Gallbladder cancer (GBC) is the most common malignant tumor in the biliary system, characterized by high malignancy, aggressiveness, and poor prognosis. Early diagnosis holds paramount importance in ameliorating therapeutic outcomes. Presently, the clinical diagnosis of GBC primarily relies on clinical-radiological-pathological approach. However, there remains a potential for missed diagnosis and misdiagnose in the realm of clinical practice. We firstly analyzed the blood-based biomarkers, such as carcinoembryonic antigen and carbohydrate antigen 19-9. Subsequently, we evaluated the diagnostic performance of various imaging modalities, including ultrasound (US), endoscopic ultrasound (EUS), computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT) and pathological examination, emphasizing their strengths and limitations in detecting early-stage GBC. Furthermore, we explored the potential of emerging technologies, particularly artificial intelligence (AI) and liquid biopsy, to revolutionize GBC diagnosis. AI algorithms have demonstrated improved image analysis capabilities, while liquid biopsy offers the promise of non-invasive and real-time monitoring. However, the translation of these advancements into clinical practice necessitates further validation and standardization. The review highlighted the advantages and limitations of current diagnostic approaches and underscored the need for innovative strategies to enhance diagnostic accuracy of GBC. In addition, we emphasized the importance of multidisciplinary collaboration to improve early diagnosis of GBC and ultimately patient outcomes. This review endeavoured to impart fresh perspectives and insights into the early diagnosis of GBC.

Comparison of clinical, radiomics, deep learning, and fusion models for predicting early recurrence in locally advanced rectal cancer based on multiparametric MRI: a multicenter study

OBJECTIVE

Predicting early recurrence (ER) in locally advanced rectal cancer (LARC) is critical for clinical decision-making. This study aimed at comparing clinical, deep learning (DL), radiomics, and two fusion models for ER prediction based on multiparametric MRI.

METHODS

This retrospective study involved 337 LARC patients from four centers between January 2016 and September 2021. Radiomics and DL features were extracted from preoperative multiparametric MRI, including T2WI, DWI, T1WI, and contrast-enhanced T1WI (CET1WI). The extreme gradient boosting (XGBoost) classifier was applied to establish the clinical model, radiomics model, DL model, and two fusion models (the feature-based early fusion model and the decision-based late fusion model). The area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA) were used to assess models. Kaplan-Meier analysis was conducted to determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of ER.

RESULTS

The late fusion model demonstrated the best performance compared with the early fusion model, clinical, radiomics and DL models, with the highest AUC (0.863-0.880) across all cohorts. In addition, the late fusion model exhibited the highest clinical net benefit, and good calibration. Kaplan-Meier survival curves showed that high-risk patients of ER defined by the late fusion model had a worse RFS than low-risk ones of ER (log-rank p < 0.001).

CONCLUSIONS

The late fusion model can accurately predict ER in LARC and may serve as a clinically useful, non-invasive tool for optimizing treatment strategies and monitoring disease progression.

Development and validation of a user-friendly prediction tool for preoperative T-Staging in gallbladder Cancer: A multicenter study using contrast-enhanced CT-Based fusion models

INTRODUCTION

Accurate T-staging of gallbladder cancer (GBC) is critical for surgical planning; however, existing imaging techniques have limited identification accuracy. This study aimed to develop a robust model and prediction tool to address these limitations.

MATERIALS AND METHODS

A retrospective cohort of 189 GBC patients from two institutions between January 2014 and December 2023 were analyzed. Patients were randomly assigned to internal training (ITC, n = 111), internal validation (IVC, n = 48), and temporal validation (TVC, n = 30) cohorts. Radiomics (Rad) and deep learning (DL) features were extracted from arterial and portal venous sequences, alongside clinical data, were used to construct pre- and post-fusion models. A weighted GBC T-staging (wGBCT) model was developed by combining probabilities from four modalities in the TVC: Clinical, Clinical + Rad(AP + PVP), Clinical + DL(AP + PVP), and Clinical + Rad + DL(CRDL), using a weighted averaging method. This model was validated and implemented as a user-friendly prediction tool.

RESULTS

The CRDL model achieved AUCs of 1.0 in the ITC and 0.913 in the IVC. In the TVC, the prediction tool attained an accuracy of 0.867, while the wGBCT model outperformed the CRDL model with an AUC of 0.910 (95 % CI: 0.792-1.000) compared to 0.869 (95 % CI:0.729-1.000). The wGBCT model also demonstrated superior sensitivity (1.0) and F1-score (0.867). Calibration curve analysis confirmed strong alignment, and decision curve analysis indicated the highest clinical net benefit at risk thresholds below 0.6.

CONCLUSIONS

The wGBCT model, integrating multimodal features and a user-friendly prediction tool, demonstrated high predictive accuracy and stability for preoperative T-staging of GBC, providing a valuable reference for individualized surgical planning.

Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma

OBJECTIVE

We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladder carcinoma (GBC).

METHODS

A total of 168 GBC patients who underwent preoperative upper abdominal enhanced CT from one high-volume medical center between January 2011 to December 2020 were retrospectively analyzed. The region of interest (ROI) was manually outlined by two physicians using 3D Slicer software to establish a nnU-Net model. The DeepSurv survival prediction model was developed by combining radiomics features and preoperative clinical variables.

RESULTS

A total of 1502 radiomics features were extracted from the ROI results based on the nnU-Net model and manual segmentation, and 13 radiomics features were obtained through the 4-step dimensionality reduction methods, respectively. The C-index and AUC of 1-, 2-, and 3-year survival prediction for the nnU-Net based clinical radiomics DeepSurv model was higher than clinical and nnU-Net based radiomics DeepSurv models in the training and testing sets, and close to manual based clinical radiomics DeepSurv model. Delong-test was performed on the AUC of 1-, 2-, and 3-year survival prediction for the two preoperative clinical radiomics DeepSurv prediction models in the testing set, and the results showed that the two models had the same prediction efficiency (all P > 0.05).

CONCLUSIONS

By using the DeepSurv model via nnU-Net segmentation, postoperative survival outcomes for individual gallbladder carcinoma patients could be assessed and stratified, which can provide references for preoperative diagnosis and treatment decisions.

Radiomic nomograms in CT diagnosis of gall bladder carcinoma: a narrative review

Radiomics is a method that extracts many features from medical images using various algorithms. Medical nomograms are graphical representations of statistical predictive models that produce a likelihood of a clinical event for a specific individual based on biological and clinical data. The radiomic nomogram was first introduced in 2016 to study the integration of specific radiomic characteristics with clinically significant risk factors for patients with colorectal cancer lymph node metastases. Thereby it gained momentum and made its way into different domains of breast, liver, and head and neck cancer. Deep learning-based radiomics which automatically generates and extracts significant features from the input data using various neural network architectures, along with the generation and usage of nomograms are the latest developments in the application of radiomics for the diagnosis of gall bladder carcinoma. Although radiomics has demonstrated encouraging outcomes in the diagnosis of gall bladder carcinoma, but most of the studies conducted suffer from a lack of external validation cohorts, smaller sample sizes, and paucity of prospective utility in routine clinical settings.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer

RATIONALE AND OBJECTIVES

An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

METHODS

A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

RESULTS

The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

CONCLUSION

The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

Discrimination between benign and malignant gallbladder lesions on enhanced CT imaging using radiomics

BACKGROUND

Gallbladder cancer is a rare but aggressive malignancy that is often diagnosed at an advanced stage and is associated with poor outcomes.

PURPOSE

To develop a radiomics model to discriminate between benign and malignant gallbladder lesions using enhanced computed tomography (CT) imaging.

MATERIAL AND METHODS

All patients had a preoperative contrast-enhanced CT scan, which was independently analyzed by two radiologists. Regions of interest were manually delineated on portal venous phase images, and radiomics features were extracted. Feature selection was performed using mRMR and LASSO methods. The patients were randomly divided into training and test groups at a ratio of 7:3. Clinical and radiomics parameters were identified in the training group, three models were constructed, and the models' prediction accuracy and ability were evaluated using AUC and calibration curves.

RESULTS

In the training group, the AUCs of the clinical model and radiomics model were 0.914 and 0.968, and that of the nomogram model was 0.980, respectively. There were statistically significant differences in diagnostic accuracy between nomograms and radiomics features (P <0.05). There was no significant difference in diagnostic accuracy between the nomograms and clinical features (P >0.05) or between the clinical features and radiomics features (P >0.05). In the testing group, the AUC of the clinical model and radiomics model were 0.904 and 0.941, and that of the nomogram model was 0.948, respectively. There was no significant difference in diagnostic accuracy between the three groups (P >0.05).

CONCLUSION

It was suggested that radiomics analysis using enhanced CT imaging can effectively discriminate between benign and malignant gallbladder lesions.

Reproducibility of radiomics quality score: an intra- and inter-rater reliability study

OBJECTIVES

To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study.

METHODS

Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used.

RESULTS

The inter-rater reliability was poor to moderate for total RQS (ICC 0.30-055, p < 0.001) and very low to good for item's reproducibility (k - 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91-0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k - 0.40 to 1).

CONCLUSIONS

Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research.

CLINICAL RELEVANCE STATEMENT

There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation.

KEY POINTS

• Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research.

Risk stratification of gallbladder masses by machine learning-based ultrasound radiomics models: a prospective and multi-institutional study

OBJECTIVE

This study aimed to evaluate the diagnostic performance of machine learning (ML)-based ultrasound (US) radiomics models for risk stratification of gallbladder (GB) masses.

METHODS

We prospectively examined 640 pathologically confirmed GB masses obtained from 640 patients between August 2019 and October 2022 at four institutions. Radiomics features were extracted from grayscale US images and germane features were selected. Subsequently, 11 ML algorithms were separately used with the selected features to construct optimum US radiomics models for risk stratification of the GB masses. Furthermore, we compared the diagnostic performance of these models with the conventional US and contrast-enhanced US (CEUS) models.

RESULTS

The optimal XGBoost-based US radiomics model for discriminating neoplastic from non-neoplastic GB lesions showed higher diagnostic performance in terms of areas under the curves (AUCs) than the conventional US model (0.822-0.853 vs. 0.642-0.706, p < 0.05) and potentially decreased unnecessary cholecystectomy rate in a speculative comparison with performing cholecystectomy for lesions sized over 10 mm (2.7-13.8% vs. 53.6-64.9%, p < 0.05) in the validation and test sets. The AUCs of the XGBoost-based US radiomics model for discriminating carcinomas from benign GB lesions were higher than the conventional US model (0.904-0.979 vs. 0.706-0.766, p < 0.05). The XGBoost-US radiomics model performed better than the CEUS model in discriminating GB carcinomas (AUC: 0.995 vs. 0.902, p = 0.011).

CONCLUSIONS

The proposed ML-based US radiomics models possess the potential capacity for risk stratification of GB masses and may reduce the unnecessary cholecystectomy rate and use of CEUS.

CLINICAL RELEVANCE STATEMENT

The machine learning-based ultrasound radiomics models have potential for risk stratification of gallbladder masses and may potentially reduce unnecessary cholecystectomies.

KEY POINTS

• The XGBoost-based US radiomics models are useful for the risk stratification of GB masses. • The XGBoost-based US radiomics model is superior to the conventional US model for discriminating neoplastic from non-neoplastic GB lesions and may potentially decrease unnecessary cholecystectomy rate for lesions sized over 10 mm in comparison with the current consensus guideline. • The XGBoost-based US radiomics model could overmatch CEUS model in discriminating GB carcinomas from benign GB lesions.

Effects of cytoreductive surgery combined with hyperthermic perfusion chemotherapy on prognosis of patients with advanced gallbladder cancer

BACKGROUND

Gallbladder cancer (GC) is a common malignant tumor and one of the leading causes of cancer-related death worldwide. It is typically highly invasive, difficult to detect in the early stages, and has poor treatment outcomes, resulting in high mortality rates. The available treatment options for GC are relatively limited. One emerging treatment modality is hyperthermic intraperitoneal chemotherapy (HIPEC). HIPEC involves delivering heated chemotherapy directly into the abdominal cavity. It combines the strategies of surgical tumor resection and localized chemotherapy administration under hyperthermic conditions, aiming to enhance the concentration and effectiveness of drugs within the local tumor site while minimizing systemic toxicity.

AIM

To determine the effects of cytoreductive surgery (CRS) combined with HIPEC on the short-term prognosis of patients with advanced GC.

METHODS

Data from 80 patients treated at the Punan Branch of Renji Hospital, Shanghai Jiao Tong University School of Medicine between January 2018 and January 2020 were retrospectively analyzed. The control group comprised 44 patients treated with CRS, and the research group comprised 36 patients treated with CRS combined with HIPEC. Then, the survival time and prognostic factors of the two groups were compared, as well as liver and kidney function indices before and six days after surgery. Adverse reactions and complications were recorded in both groups.

RESULTS

The baseline data of the research and control groups were similar (P > 0.05). Six days after surgery, the alanine aminotransferase, aspartate aminotransferase, total bilirubin, and direct bilirubin levels significantly decreased compared to the preoperative levels in both groups (P < 0.05). However, the values did not differ between the two groups six days postoperatively (P > 0.05). Similarly, the postoperative creatinine and blood urea nitrogen levels were significantly lower than the preoperative levels in both groups (P < 0.05), but they did not differ between the groups six days postoperatively (P > 0.05). Furthermore, the research group had fewer postoperative adverse reactions than the control group (P = 0.027). Finally, a multivariate Cox analysis identified the tumor stage, distant metastasis, and the treatment plan as independent factors affecting prognosis (P < 0.05). The three-year survival rate in the study group was higher than that in the control group (P = 0.002).

CONCLUSION

CRS combined with HIPEC lowers the incidence of adverse reactions and improves survival in patients with advanced GC.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study

OBJECTIVES

To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC).

METHODS

A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence.

RESULTS

The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence.

CONCLUSIONS

The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients.

CLINICAL RELEVANCE STATEMENT

An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients.

KEY POINTS

• The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study

PURPOSE

Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection.

METHODS

Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.

RESULTS

127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03).

CONCLUSION

ML radiomics models based on CECT are valuable in predicting ER in ICC.

Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study

PURPOSE

To verify whether radiomics techniques based on dual-modality ultrasound consisting of B-mode and superb microvascular imaging (SMI) can improve the accuracy of the differentiation between gallbladder neoplastic polyps and cholesterol polyps.

METHODS

A total of 100 patients with 100 pathologically proven gallbladder polypoid lesions were enrolled in this retrospective study. Radiomics features on B-mode ultrasound and SMI of each lesion were extracted. Support vector machine was used to classify adenomas and cholesterol polyps of gallbladder for B-mode, SMI and dual-modality ultrasound, respectively, and the classification results were compared among the three groups.

RESULTS

Six, eight and nine features were extracted for each lesion at B-mode ultrasound, SMI and dual-modality ultrasound, respectively. In dual-modality ultrasound model, the area under the receiver operating characteristic curve (AUC), classification accuracy, sensitivity, specificity, and Youden's index were 0.850 ± 0.090, 0.828 ± 0.097, 0.892 ± 0.144, 0.803 ± 0.149 and 0.695 ± 0.157, respectively. The AUC and Youden's index of the dual-modality model were higher than those of the B-mode model (p < 0.05). The AUC, accuracy, specificity and Youden's index of the dual-modality model were higher than those of the SMI model (p < 0.05).

CONCLUSIONS

Radiomics analysis of the dual-modality ultrasound composed of B-mode and SMI can improve the accuracy of classification between gallbladder neoplastic polyps and cholesterol polyps.