eISSN: 2449-8238
ISSN: 2392-1099
Clinical and Experimental Hepatology
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abstract:
Original paper

Deep learning-based classification of gallbladder lesions in patients with non-diagnostic (GB-RADS 0) ultrasound

Pankaj Gupta
1
,
Ruby Siddiqui
1
,
Thakur D. Yadav
1
,
Lileswar Kaman
1
,
Gaurav Prakash
1
,
Parikshaa Gupta
1
,
Uma N. Saikia
1
,
Usha Dutta
1

  1. PGIMER, Chandigarh, India
Clin Exp HEPATOL 2024; 10, 4:
DOI: https://doi.org/10.5114/ceh.2024.145424
Online publish date: 2024/12/11
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Aim of the study:
Non-diagnostic ultrasound (US) of the gallbladder may be due to various factors. We aimed to evaluate the diagnostic performance of deep learning-based classification of gallbladder lesions on US images in patients with non-diagnostic US secondary to gallbladder factors.

Material and methods:
Consecutive patients with non-diagnostic US due to calculi within the gallbladder lumen, obscuring the detailed evaluation, were identified by a research fellow from a prospective database of patients with gallbladder lesions. The US reports and images were evaluated by a radiologist blinded to the final diagnosis. Patients who had the final pathological diagnosis based on fine-needle aspiration cytology, percutaneous or endoscopic biopsy, or surgical histopathology were included. Convolution neural networks (ResNet50, GBCNet), transformer models (vision transformer [ViT], RadFormer), and a hybrid model (MedViT) were trained on a public gallbladder dataset (GBCU dataset). The performance of these models for classifying gallbladder lesions into benign and malignant was tested on non-diagnostic (GB-RADS 0) US images.

Results:
Training and validation cohorts (GBCU dataset) comprised 1004 and 251 images, respectively. The testing data (26 patients, mean age [SD]: 57.5 ±8.07 years, 17 female) comprised 304 images. The best performance for detection of GBC was achieved with GBCNet (sensitivity 51.1%, specificity 83.3%, area under the curve [AUC] 0.709) and MedViT (sensitivity 92.8%, specificity 50%, AUC 0.714). MedViT had the best accuracy (73.1%) for detecting benign gallbladder lesions.

Conclusions:
These results suggest that deep learning models can potentially stratify patients with non-diagnostic US. However, further improvement in the performance is needed to render this approach relevant in clinical practice.

keywords:

deep learning, gallbladder cancer, ResNet 50, vision transformer, ultrasound
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