Risk factors to discriminate neoplastic polypoid lesions of gallbladder: A large scale, case-series study

Risk prediction and analysis of gallbladder polyps with deep neural network

The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People's Hospital of Shenzhen between January 2017 and December 2022. The patients' clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI -0.237 to 0.061, p < 0.001), number of polyps (95% CI -0.214 to -0.052, p = 0.001), polyp size (95% CI 0.038 to 0.051, p < 0.001), wall thickness (95% CI 0.042 to 0.081, p < 0.001), and gallbladder size (95% CI 0.185 to 0.367, p < 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, Predictive classification model for GBPs = -0.149 * core antibody - 0.033 * number of polyps + 0.045 * polyp size + 0.061 * wall thickness + 0.276 * gallbladder size - 4.313. To assess the predictive efficiency of the model, we employed precision-recall (PR) and receiver operating characteristic (ROC) curves. The area under the curve (AUC) for the prediction model was 0.945 and 0.930, respectively, indicating excellent predictive capability. We determined that a polyp size of 10 mm served as the optimal cutoff value for diagnosing gallbladder adenoma, with a sensitivity of 81.5% and specificity of 60.0%. For the diagnosis of gallbladder cancer, the sensitivity and specificity were 81.5% and 92.5%, respectively. These findings highlight the potential of our predictive model and provide valuable insights into accurate diagnosis and risk assessment for gallbladder polyps. We identified several risk factors associated with the development of adenomatous and malignant polyps in the gallbladder, including hepatitis B core antibodies, polyp number, polyp size, wall thickness, and gallbladder size. To address the need for accurate prediction, we introduced a novel neural network learning algorithm. This algorithm utilizes the aforementioned risk factors to predict the nature of gallbladder polyps. By accurately identifying the nature of these polyps, our model can assist patients in making informed decisions regarding their treatment and management strategies. This innovative approach aims to improve patient outcomes and enhance the overall effectiveness of care.

Management of incidentally detected gallbladder polyps: a review of clinical scenarios using the 2022 SRU gallbladder polyp consensus guidelines

Gallbladder (GB) polyps are a common incidental finding on sonography, but only a small fraction of polyps become GB cancer. The Society of Radiologists in Ultrasound (SRU) consensus committee recently performed an extensive literature review and published guidelines for GB polyp follow-up/management to provide clarity among the many heterogeneous recommendations that are available to clinicians. As these guidelines have become adopted into clinical practice, challenging clinical scenarios have arisen including GB polyps in primary sclerosing cholangitis (PSC), high risk geographic/genetic patient populations, shrinking polyps, pedunculated vs sessile polyps, thin vs thick stalked polyps, vascular polyps and multiple polyps. According to the SRU guidelines, clinicians should refer to gastroenterology guidelines when managing GB polyps in patients with known PSC. If patients at high geographic/genetic risk develop GB polyps, 'extremely low risk' polyps may be managed as 'low risk' and 10-14 mm 'extremely low risk' or '7-14 mm' low risk polyps that decrease in size by ≥ 4 mm require no follow-up. Thin-stalked or pedunculated polyps are 'extremely low risk' and thick-stalked pedunculated polyps are 'low risk'. Sessile polyps are 'low risk' but should receive immediate specialist referral if features suggestive of GB cancer are present. Neither polyp multiplicity nor vascularity impact risk of GB cancer and follow up should be based on morphology alone.

Analysis of ultrasonographic images using a deep learning-based model as ancillary diagnostic tool for diagnosing gallbladder polyps

BACKGROUND

Accurately diagnosing gallbladder polyps (GBPs) is important to avoid misdiagnosis and overtreatment.

AIMS

To evaluate the efficacy of a deep learning model and the accuracy of a computer-aided diagnosis by physicians for diagnosing GBPs.

METHODS

This retrospective cohort study was conducted from January 2006 to September 2021, and 3,754 images from 263 patients were analyzed. The outcome of this study was the efficacy of the developed deep learning model in discriminating neoplastic GBPs (NGBPs) from non-NGBPs and to evaluate the accuracy of a computer-aided diagnosis with that made by physicians.

RESULTS

The efficacy of discriminating NGBPs from non- NGBPs using deep learning was 0.944 (accuracy, 0.858; sensitivity, 0.856; specificity, 0.861). The accuracy of an unassisted diagnosis of GBP was 0.634, and that of a computer-aided diagnosis was 0.785 (p<0.001). There were no significant differences in the accuracy of a computer-aided diagnosis between experienced (0.835) and inexperienced (0.772) physicians (p = 0.251). A computer-aided diagnosis significantly assisted inexperienced physicians (0.772 vs. 0.614; p < 0.001) but not experienced physicians.

CONCLUSIONS

Deep learning-based models discriminate NGBPs from non- NGBPs with excellent accuracy. As ancillary diagnostic tools, they may assist inexperienced physicians in improving their diagnostic accuracy.

A Bayesian network model to predict neoplastic risk for patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features

BACKGROUND

Polyp size of 10 mm is insufficient to discriminate neoplastic and non-neoplastic risk in patients with gallbladder polyps (GPs). The aim of the study is to develop a Bayesian network (BN) prediction model to identify neoplastic polyps and create more precise criteria for surgical indications in patients with GPs lager than 10 mm based on preoperative ultrasound features.

METHODS

A BN prediction model was established and validated based on the independent risk variables using data from 759 patients with GPs who underwent cholecystectomy from January 2015 to August 2022 at 11 tertiary hospitals in China. The area under receiver operating characteristic curves (AUCs) were used to evaluate the predictive ability of the BN model and current guidelines, and Delong test was used to compare the AUCs.

RESULTS

The mean values of polyp cross-sectional area (CSA), long, and short diameter of neoplastic polyps were higher than those of non-neoplastic polyps (P < 0.0001). Independent neoplastic risk factors for GPs included single polyp, polyp CSA ≥ 85 mm ², fundus with broad base, and medium echogenicity. The accuracy of the BN model established based on the above independent variables was 81.88% and 82.35% in the training and testing sets, respectively. Delong test also showed that the AUCs of the BN model was better than that of JSHBPS, ESGAR, US-reported, and CCBS in training and testing sets, respectively (P < 0.05).

CONCLUSION

A Bayesian network model was accurate and practical for predicting neoplastic risk in patients with gallbladder polyps larger than 10 mm based on preoperative ultrasound features.

RadioGraphics Update: New Follow-up and Management Recommendations for Polypoid Lesions of the Gallbladder

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new informaion such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

Management of Incidentally Detected Gallbladder Polyps: Society of Radiologists in Ultrasound Consensus Conference Recommendations

Gallbladder polyps (also known as polypoid lesions of the gallbladder) are a common incidental finding. The vast majority of gallbladder polyps smaller than 10 mm are not true neoplastic polyps but are benign cholesterol polyps with no inherent risk of malignancy. In addition, recent studies have shown that the overall risk of gallbladder cancer is not increased in patients with small gallbladder polyps, calling into question the rationale for frequent and prolonged follow-up of these common lesions. In 2021, a Society of Radiologists in Ultrasound, or SRU, consensus conference was convened to provide recommendations for the management of incidentally detected gallbladder polyps at US. See also the editorial by Sidhu and Rafailidis in this issue.