Dual-energy CT in differentiating benign gallbladder wall thickening from wall thickening type of gallbladder cancer.

OBJECTIVE: To evaluate the performance of dual-energy computed tomography (DECT) in differentiating non-acute benign from malignant gallbladder wall thickening (GBWT). METHODS: This prospective study comprised consecutive adults with GBWT who underwent late arterial phase (LAP) and portal venous phase (PVP) DECT between January 2022 and May 2023. The final diagnosis was based on histopathology or 3-6 months follow-up imaging. DECT images in LAP and PVP were assessed independently by two radiologists. The demographic, qualitative, and quantitative parameters were compared between two groups Multivariate logistic regression was performed to determine the association between the aforementioned factors and malignant GBWT. RESULTS: Seventy-five patients (mean age 56 ± 12.8 years, 46 females) were included. Forty-two patients had benign, and 33 had malignant GBWT. In the overall group, female gender (p = 0.018), lymphadenopathy (p = 0.011), and omental nodules (p = 0.044) were significantly associated with malignant GBWT. None of the DECT features differed significantly between benign and malignant GBWT in overall group. In the xanthogranulomatous cholecystitis (XGC, n = 9) vs. gallbladder cancer (GBC) (n = 33) subgroup, mean attenuation value at 140 keV LAP VMI was significantly associated with malignant GBWT [p = 0.023, area under curve 0.759 (95%CI 0.599-0.919)]. CONCLUSION: DECT-generated quantitative parameters do not add value in differentiating non-acute benign from malignant GBWT. However, DECT may have a role in differentiating XGC from GBC in a selected subgroup of patients. Further, larger studies may be necessary to confirm these findings. CLINICAL RELEVANCE STATEMENT: In patients with non-acute gallbladder wall thickening in whom there is suspicion of xanthogranulomatous cholecystitis (XGC), DECT findings may allow differentiation of XGC from wall thickening type of gallbladder cancer. KEY POINTS: Differentiation of benign and malignant gallbladder wall thickening (GBWT) at CT is challenging. Quantitative dual energy CT (DECT) features do not provide additional value in differentiating benign and malignant GBWT. DECT may be helpful in a subgroup of patients to differentiate xanthogranulomatous cholecystitis from gallbladder cancer.

Contrast Enhanced CT Versus MRI for Accurate Diagnosis of Wall-thickening Type Gallbladder Cancer.

Introduction: Diagnosis of wall-thickening type gallbladder cancer (GBC) is challenging. Computed tomography (CT) and magnetic resonance imaging (MRI) are commonly utilized to evaluate gallbladder wall thickening. However, there is a lack of data comparing the performance of CT and MRI for the detection of wall-thickening type GBC. Aim: We aim to compare the diagnostic accuracy of CT and MRI in diagnosis of wall-thickening type GBC. Materials and methods: This prospective study comprised consecutive patients suspected of wall-thickening type GBC who underwent preoperative contrast-enhanced CT and MRI. The final diagnosis was based on the histopathology of the resected gallbladder lesion. Two radiologists independently reviewed the characteristics of gallbladder wall thickening at CT and MRI. The association of CT and MRI findings with histological diagnosis and the interobserver agreement of CT and MRI findings were assessed. Results: Thirty-three patients (malignancy, 13 and benign, 20) were included. None of the CT findings were significantly associated with GBC. However, at MRI, heterogeneous enhancement, indistinct interface with the liver, and diffusion restriction were significantly associated with malignancy (P = 0.006, <0.001, and 0.005, respectively), and intramural cysts were significantly associated with benign lesions (P = 0.012). For all MRI findings, the interobserver agreement was substantial to perfect (kappa = 0.697-1.000). At CT, the interobserver agreement was substantial to perfect (k = 0.631-1.000). Conclusion: These findings suggest that MRI may be preferred over CT in patients with suspected wall thickening type GBC. However, larger multicenter studies must confirm our findings.

Deep-learning enabled ultrasound based detection of gallbladder cancer in northern India: a prospective diagnostic study.

Background: Gallbladder cancer (GBC) is highly aggressive. Diagnosis of GBC is challenging as benign gallbladder lesions can have similar imaging features. We aim to develop and validate a deep learning (DL) model for the automatic detection of GBC at abdominal ultrasound (US) and compare its diagnostic performance with that of radiologists. Methods: In this prospective study, a multiscale, second-order pooling-based DL classifier model was trained (training and validation cohorts) using the US data of patients with gallbladder lesions acquired between August 2019 and June 2021 at the Postgraduate Institute of Medical Education and research, a tertiary care hospital in North India. The performance of the DL model to detect GBC was evaluated in a temporally independent test cohort (July 2021-September 2022) and was compared with that of two radiologists. Findings: The study included 233 patients in the training set (mean age, 48 ± (2SD) 23 years; 142 women), 59 patients in the validation set (mean age, 51.4 ± 19.2 years; 38 women), and 273 patients in the test set (mean age, 50.4 ± 22.1 years; 177 women). In the test set, the DL model had sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 92.3% (95% CI, 88.1-95.6), 74.4% (95% CI, 65.3-79.9), and 0.887 (95% CI, 0.844-0.930), respectively for detecting GBC which was comparable to both the radiologists. The DL-based approach showed high sensitivity (89.8-93%) and AUC (0.810-0.890) for detecting GBC in the presence of stones, contracted gallbladders, lesion size <10 mm, and neck lesions, which was comparable to both the radiologists (p = 0.052-0.738 for sensitivity and p = 0.061-0.745 for AUC). The sensitivity for DL-based detection of mural thickening type of GBC was significantly greater than one of the radiologists (87.8% vs. 72.8%, p = 0.012), despite a reduced specificity. Interpretation: The DL-based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting GBC using US. However, multicentre studies are warranted to explore the potential of DL-based diagnosis of GBC fully. Funding: None.