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

MiR-33 as a novel diagnostic biomarker for distinguishing cholesterol from adenomatous polyps: a case-control study

Cholecystectomy is often excessively utilized in the management of gallbladder polyps. It is crucial to effectively differentiate between adenomatous and cholesterol polyps to reduce unnecessary cholecystectomies. This study aimed to investigate the potential of miR-33 as a novel diagnostic biomarker for distinguishing cholesterol from adenomatous polyps. Gallbladder specimens were retrospectively collected from gallbladder polyp patients who underwent laparoscopic cholecystectomy at the Second Department of General Surgery, Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, between June 2021 and December 2021. Pathological analysis categorized the specimens into two groups: the cholesterol polyp group (n = 13) and the adenomatous polyp group (n = 12). The expression levels of miR-33a and miR-33b in both groups were assessed using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). MiR-33a level and the miR-33a/miR-33b ratio were significantly lower in cholesterol polyps than in adenomatous polyps (p < 0.05). Spearman correlation analysis showed a strong positive correlation between miR-33a and miR-33b (r = 0.956, p < 0.001). Stepwise logistic regression analysis revealed that decreased miR-33b and elevated miR-33a/miR-33b ratio are independent risk factors for cholesterol polyps (p < 0.05). A predictive model was constructed, with the model's AUC for diagnosing adenomatous polyps being 0.885 (95% CI: 0.753-1.000, p = 0.001), exhibiting a notable specificity of 84.62% and a sensitivity of 83.33% at a cut-off of 0.424. MiR-33 could serve as a novel diagnostic biomarker for distinguishing cholesterol from adenomatous polyps to facilitate the diagnosis and treatment of clinicians.

Development and validation of a preoperative nomogram for predicting gallbladder adenoma

BACKGROUND

Gallbladder cholesterol polyp (GCP) and gallbladder adenoma (GA) are easily confused in clinical diagnosis. This study aims to establish a nomogram prediction model for preoperative prediction of the risk of GA patients.

STUDY DESIGN

We retrospectively collected clinical data of GCP or GA patients who underwent laparoscopic cholecystectomy (LC) between January 2020 and April 2023. We compared and analyzed the differences between the GCP group and the GA group. The data were divided into a training set and a validation set in a 7:3 ratio. Independent risk factors were determined using LASSO and Logistic regression analysis, and a nomogram model was established. The model was comprehensively validated and evaluated using the area under the ROC curve (AUC), Hosmer-Lemeshow test and clinical decision curve analysis (DCA).

RESULTS

This study ultimately included 497 patients. The independent predictors of the nomogram model include blood type (O-type blood, OR 2.00, 95% CI 1.02-3.94; P = 0.046), number of lesions (solitary, OR 2.11; 95% CI 1.08-4.12; P = 0.033), sessile polyp (OR 2.04; 95% CI 1.06-3.92; P = 0.033), age (OR 1.10; 95% CI 1.07-1.20; P < 0.001), diameter (OR 1.30; 95% CI 1.17-1.45; P < 0.001). For the training and validation set, the area under the ROC curve (AUC) was 0.843 and 0.837, respectively, and the P-value for the Hosmer-Lemeshow test was 0.056 and 0.300, respectively. In addition, the calibration curve and DCA curve indicate that the model has accurate predictive ability and reliable clinical practicality.

CONCLUSIONS

The blood type, number of lesions, sessile polyp, age and diameter are significant risk factors for GA. This nomogram model can use simple and readily available clinical data to predict the risk of having GA and can assist in guiding surgical decisions.

Non-invasive classification of non-neoplastic and neoplastic gallbladder polyps based on clinical imaging and ultrasound radiomics features: An interpretable machine learning model

BACKGROUND

Gallbladder (GB) adenomas, precancerous lesions for gallbladder carcinoma (GBC), lack reliable non-invasive tools for preoperative differentiation of neoplastic polyps from cholesterol polyps. This study aimed to evaluate an interpretable machine learning (ML) combined model for the precise differentiation of the pathological nature of gallbladder polyps (GPs).

METHODS

This study consecutively enrolled 744 patients from Xi'an Jiaotong University First Affiliated Hospital between January 2017 and December 2023 who were pathologically diagnosed postoperatively with cholesterol polyps, adenomas or T1-stage GBC. Radiomics features were extracted and selected, while clinical variables were subjected to univariate and multivariate logistic regression analyses to identify significant predictors of neoplastic polyps. A optimal ML-based radiomics model was developed, and separate clinical, US and combined models were constructed. Finally, SHapley Additive exPlanations (SHAP) was employed to visualize the classification process.

RESULTS

The areas under the curves (AUCs) of the CatBoost-based radiomics model were 0.852 (95 % CI: 0.818-0.884) and 0.824 (95 % CI: 0.758-0.881) for the training and test sets, respectively. The combined model demonstrated the best performance with an improved AUC of 0.910 (95 % CI: 0.885-0.934) and 0.869 (95 % CI: 0.812-0.919), outperformed the clinical, radiomics, and US model (all P < 0.05), and reduced the rate of unnecessary cholecystectomies. SHAP analysis revealed that the polyp short diameter is a crucial independent risk factor in predicting the nature of the GPs.

CONCLUSION

The ML-based combined model may be an effective non-invasive tool for improving the precision treatment of GPs, utilizing SHAP to visualize the classification process can enhance its clinical application.

Development and Validation of a Preoperative Prediction Model for Neoplastic Gallbladder Polyps

Aims/Background The primary goal in evaluating gallbladder polypoid lesions (GPLs) is to identify neoplastic polyps (NP). Numerous studies have investigated risk factors for NP. This study aimed to develop a practical preoperative prediction model for NP using simple and easily accessible clinical variables. Methods We retrospectively analyzed clinical data from patients with GPLs who underwent cholecystectomy at Lanzhou University Second Hospital between January 2018 and September 2022. A total of 621 cases were included and randomly assigned into a training set (70%) and an internal validation set (30%). An external validation set was established using data from 117 patients treated at other centers between January and December 2023. Univariate logistic analyses were performed, followed by backward stepwise multivariate logistic regression analysis for variables with p< 0.2 to identify significant variables associated with NP. These predictors were included in the final logistic regression model and visualized as a nomogram model. The discrimination, calibration, and clinical utility of the model were evaluated. Results Age (odd ratio (OR) = 1.06, 95% CI = 1.03-1.09, p= 0.0001), polyp size (OR = 19.01, 95% CI = 6.48-55.79, p < 0.0001), polyp number (OR = 0.25, 95% CI = 0.12-0.56, p = 0.0006), gallbladder wall thickness (OR = 1.57, 95% CI = 1.02-2.41, p= 0.0385), and polyp echo characteristics (OR = 0.41, 95% CI = 0.19-0.85, p = 0.0169) were identified as independent influencing factors for NP. The area under the curve (AUC) of the nomogram model in the training, internal validation, and external validation sets were 0.886 (95% CI, 0.841-0.930), 0.836 (95% CI, 0.753-0.919), and 0.867 (95% CI, 0.743-0.978), respectively. Calibration curves for the three datasets showed Brier scores of 0.079, 0.092, and 0.070, all below 0.25, indicating good calibration. Decision curve analysis (DCA) and clinical impact curve (CIC) analysis suggested that a threshold probability of 0.6 provided the most significant clinical benefit. Conclusion This prediction model, incorporating easily accessible variables, demonstrated excellent performance in the identification of NP and contributed to clinical decision-making in GPL management.

Interpretation, Reporting, Imaging-Based Workups, and Surveillance of Incidentally Detected Gallbladder Polyps and Gallbladder Wall Thickening: 2025 Recommendations From the Korean Society of Abdominal Radiology

Incidentally detected gallbladder polyps (GBPs) and gallbladder wall thickening (GBWT) are frequently encountered in clinical practice. However, characterizing GBPs and GBWT in asymptomatic patients can be challenging and may result in overtreatment, including unnecessary follow-ups or surgeries. The Korean Society of Abdominal Radiology (KSAR) Clinical Practice Guideline Committee has developed expert recommendations that focus on standardized imaging interpretation and follow-up strategies for both GBPs and GBWT, with support from the Korean Society of Radiology and KSAR. These guidelines, which address 24 key questions, aim to standardize the approach for the interpretation of imaging findings, reporting, imaging-based workups, and surveillance of incidentally detected GBPs and GBWT. This recommendation promotes evidence-based practice, facilitates communication between radiologists and referring physicians, and reduces unnecessary interventions.

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.

Plain Computed Tomography for Differentiating Neoplastic and Non-neoplastic Pedunculated Gallbladder Polyps

Objective Surgery is recommended for large pedunculated gallbladder polyps (PGPs), which measure 10 mm or more in size, because they tend to be neoplastic polyps (NPs), such as adenomas and adenocarcinomas. However, after resection, they are often found to be non-neoplastic polyps (non-NPs). This study aimed to evaluate the usefulness of plain computed tomography (CT) in distinguishing NPs from non-NPs. Methods Of the 80 patients who underwent cholecystectomy for PGPs ≥10 mm between January 2008 and February 2021, 46 who underwent plain and contrast-enhanced CT (CE-CT) before resection were included in this study. We retrospectively assessed the polyp detection rate (PDR) using CT and calculated the difference in the CT values between PGPs and the surrounding bile. Results Twenty-one patients had NPs (12 adenomas, 5 carcinomas in adenoma, and 4 adenocarcinomas). The others were non-NPs (24 cholesterol polyps and one hyperplastic polyp). The PDR using plain CT was significantly higher in the NP group than in the non-NP group (38% (8/21) vs. 0% (0/25), p <0.01). The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of NPs were 38%, 100%, 100%, 66%, and 72%, respectively. The difference in the CT values between PGPs and the surrounding bile was significantly larger in the NP group than in the non-NP group (14.12 ± 11.38 HU, 5.04 ± 6.15 HU, p <0.01). Conclusions PGPs detected using plain CT had a high probability of being NPs. Plain CT is therefore considered to be useful for differentiating NPs from non-NPs.

Diagnostic performance of EUS-guided elastography for differential diagnosis of gallbladder polyp

BACKGROUND AND AIMS

It is difficult to differentiate between neoplastic and non-neoplastic gallbladder (GB) polyps before surgery. EUS-guided elastography (EUS-EG) is a noninvasive complementary diagnostic method. The utility of EUS-EG in the differential diagnosis of GB polyps has not been investigated. We investigated the diagnostic performance of EUS-EG for the differential diagnosis of GB polyps.

METHODS

Patients with GB polyps were prospectively enrolled from June 2020 until November 2022. EUS-EG and semiquantitative evaluation of the strain ratio (SR) were performed for differential diagnosis of GB polyps. Fifty-three eligible patients were divided into 2 groups based on the final diagnosis after surgery. Patient demographics, EUS characteristics, and SR values were compared. A receiver-operating characteristic curve analysis was performed to determine the optimal cutoff SR value that discriminates between neoplastic and non-neoplastic GB polyps.

RESULTS

The median SR value for neoplastic polyps (32.93 [interquartile range {IQR}, 22.37-69.02]) was significantly higher than for non-neoplastic polyps (5.40 [IQR, 2.36-14.44], P < .001). Significant differences were found in SR values between non-neoplastic, benign neoplastic (23.38 [IQR, 13.62-39.04]), and malignant polyps (49.25 [IQR, 27.90-82.00]). The optimal cutoff SR value to differentiate between neoplastic and non-neoplastic polyps was 18.4. In multivariable logistic regression, SR value >18.4 (odds ratio, 33.604; 95% confidence interval, 2.588-436.292) was an independent predictor of neoplastic polyps.

CONCLUSIONS

EUS-EG and SR values can be used as a supplementary method for evaluating GB polyps. (Clinical trial registration number: NCT04416763.).

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.

Comparison of the Size Measurement of Gallbladder Polyps by Three Different Radiologists in Abdominal Ultrasonography

BACKGROUND

There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US).

AIM

To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US.

METHODS

From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland-Altman plot was used to visualize the differences between the first and second size measurements in each reader.

RESULTS

Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers.

CONCLUSIONS

GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error.

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.