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Adhesions detection and staging classification for preoperative assessment of difficult laparoscopic cholecystectomies: A prospective case–control study

1 Department of Radiology, Advanced Diagnostics, Amritsar, Punjab, India
2 Department of Surgery, Parwati Hospital and Sukh Sagar Hospital, Amritsar, Punjab, India

Correspondence Address:
Atul Kapoor,
Department of Radiology, Advanced Diagnostics, 17/7, Kennedy Avenue, Amritsar, Punjab
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Source of Support: None, Conflict of Interest: None

Background: Laparoscopic cholecystectomy (LC) is the treatment of choice for cholelithiasis; however, there are procedural difficulties in determining preoperative detection of a difficult LC. The current methods using clinical and sonographic variables to identify difficult LCs have limitations to identify gallbladder adhesions which form the most common cause. We present a new method of evaluation using acoustic radiation force impulse (ARFI)-based virtual touch imaging (VTI) for the detection and classification of these patients. Methods: Fifty consecutive patients of cholelithiasis were evaluated preoperatively using conventional scoring system (CSS) and by new adhesion detection and staging (ADS) system, and patients were classified into three classes (I–III) with class I being easy, II and III being moderate-to-high difficulty LCs. Peroperative classification was done based on the difficulty level during surgery after visualization of gallbladder adhesions. The sensitivity, specificity, and area under the curves (AUCs) of both systems were compared. Results: Out of 50 patients, 72% and 54% of patients were in class I by CSS and ADS classification, while 28% and 46% were in class II and III, respectively, and were labeled as difficult LC cases; differences being two classifications were statistically significant (P = 0.02). Sensitivity, specificity, negative predictive value, and accuracy for ADS were 91%, 100%, 93.1%, and 96.0%, and for CSS, 60.9%, 100%, 75%, and 82% with AUCs of 1.0 and 0.63, respectively. Conclusion: ARFI-based VTI accurately detects gallbladder adhesions and can determine the difficult cases of LCs preoperatively using ADS classification and shows higher accuracy than CSS classification, which results in lower operative time and risk of complications.

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