The presence of a mass in the gallbladder is one of the most challenging and common findings in the field of hepatobiliary surgery. A simple cholecystectomy is routinely performed with the assumption that most are BGMLs. However, a majority of the localized GBC are diagnosed incidentally during a pathological examination after surgery. Unfortunately, this situation renders patients to undergo completion resection depending on the cancer stage, except those with T1a, which may be treated by simple cholecystectomy without surgical reintervention (19). Thus, preoperative differentiation of patients who are classified as high-risk group for GBC is critical. This allows the general surgeons to consider referral to a team of more specialized hepatobiliary surgeons upfront. In addition, this knowledge helps the surgeons to further define T-stage and status of the cystic duct margin as well as their decision to perform a partial hepatectomy, lymphadenectomy, and/or excision of the extrahepatic biliary tree. Finally, early proper preoperative planning may allow to cut down the risk of bile spillage, a life-threatening event in the case of GBC.
There has been an increasing use of nomograms as diagnostic and prognostic tools to help guide clinical decision-making in oncology (31–33). They incorporated multiple factors with more accuracy given the complex nature and biological processes of malignancy (34). Recently, nomograms by CT have been shown to be superior to the traditional methods (34,35). Unlike a biopsy, a CT scan is noninvasive. However, its clinical significance was limited because of the subjective interpretations and qualitative in nature. Leijenaar et al. (35) developed a signature feature to predict human papillomavirus status from standard CT imaging. Larue et al. (36) used pretreatment CT image to predict 3-year overall survival after chemoradiotherapy of esophageal cancer. In the present study, we have developed a diagnostic nomogram with a combination of the multicomponent radiological features and independent clinical factors. We demonstrated that the AUC (0.89) was higher than that of the previous method (23). It helped to rule out some benign diseases such as chronic granulomatous cholecystitis, adenomyomatosis, and acute and chronic cholecystitis, which can also cause similar single- or double-layer patterns on an enhanced CT (37).
In this study, the nomogram was established by 6 radiological features selected by LASSO statistics and 2 clinical factors. The radiological features such as the size of the mass, with or without gallbladder stones, mucosal smoothness, and enhanced gallbladder wall were widely recognized as the diagnostic characteristics of GBC. The anatomic layers of the gallbladder wall are similar to those of the gastrointestinal wall and, so, are the progression of GBC and that of gastrointestinal tract adenocarcinoma. The enhancement patterns of diffusing gastric cancers may be caused mainly by fibrous stroma in tumors (38). Layered patterns of the gallbladder wall on the PVP may also be attributed to intratumoral fibrous stroma (39). It is worthy to mention that most gallbladder carcinomas are associated with heterogeneous or indistinguishable signal intensity without layering (single-layer pattern) (31,40). We speculated that widespread infiltrative tumor cells with the interstitial fiber components could have contributed to the feature. Therefore, when the contrast agent passes the interface, it diffuses slower from vessels to the fibrous stroma than normal (41), showing a single-layer and enhanced gallbladder wall. In addition, acute cholecystitis usually has a mixture of mature fibrosis (abundant collagen fibers combined with scattered vessels and cells) and immature fibrosis (plenteous neovascularization and fibroblasts) in the gallbladder wall so that they are with both weak enhancement and good enhancement, respectively, on the PVP. Also, the value of ΔCT (portal phase − delayed phase) is critical to identify a malignancy. It can reflect the biological processes of tumor including its formation and accumulation of fibrosis more intuitively. However, molecular changes that arose from the progression and intratumoral events are less well detected and characterized than those in cancer (42). In this study, we identified 2 significant clinical factors—age and CA19.9 levels in addition to image factors. The possibility of malignancy increased with age (high, >70; medium, 50–70; and low, <50) and higher CA19.9 levels, a widely accepted tumor marker for GBC. The establishment of nomogram with both clinical and radiological factors added significant strength for early detection of malignancy in the gallbladder, especially for T1-2 tumors.
In fact, most previous studies (12,23,24,28–30) presented some main factors, such as “cotton ball sign” and wall thickness and enhancement. Compared with them, Zhou's model, based on the ΔCT value for GBC, showed a higher sensitivity and specificity. Therefore, we compared our model with Zhou's model, even combined with clinical factors. In addition, to know whether our model (clinical factors combined with radiological features) was superior to either of them alone, we compared our model with each one. Notably, the external validation cohort was used to compare each model/method, and our model got superior sensitivity and accuracy.
Patients with T4 disease and/or with liver metastasis can be staged adequately with routine scanning. However, it is extremely challenging to diagnose early-stage GBC, usually with T1/T2 stages, that accounts for more than 40% of GBC misdiagnosis (43). Fortunately, our novel nomogram can assist in distinguishing nearly 60% of misdiagnosed IGBC. According to the cutoff value of 82 (probability of malignancy, 50%), we classified the patients into low- and high-risk subgroups. Using this nomogram, most benign lesions were classified as low-risk with diagnostic accuracy not inferior to that of radiologists, and more than 85% of GBC as high risk as a whole. This indicates that the diagnostic accuracy of GBC had been improved greatly with our model. Interestingly, using the nomogram, we enabled to detect malignant gallbladder mass lesions preoperatively; in other words, we could conduct preoperative qualitative analysis of mass lesions (benign or malignant).
The strengths of this study are as follows. First, this is the first multicenter study performed to distinguish BGMLs and GBC. We evaluated and analyzed the layered patterns of the gallbladder wall using contrast-enhanced CT and assessed its significance in the differentiation of benign and malignant diseases. Besides, our radiological features were extracted from 3 parts, which consisted of the mass, gallbladder wall characteristics, and outside of the gallbladder. By contrast, only the single parts had been used in previous studies. In addition, we used the ΔCT value to evaluate the enhancement of the gallbladder mass more intuitively.
There are some limitations in the present study. First, it is a retrospective study, which consisted of a small number of patient samples, and we have not used liver invasion, bile duct invasion, and hepatic artery invasion in the model. Therefore, the model needs to be validated by larger studies with adjacent organ invasion to some extent. Second, patients with rare diseases such as bile duct abnormalities and gallbladder abnormalities were excluded from the study due to the fact that most patients with few basic diseases diagnosed as gallbladder mass lesions were referred to ambulatory surgery in our center. With more patients comorbid with rare diseases in our center, we would include more patients to improve the diagnostic accuracy of the model. Besides, although the value of the AUC reached to 0.89 and all kappa coefficients were greater than 0.76, some features were not recommended to extracted by CT scan. To avoid some errors from CT scan, Doppler ultrasound was used to double check the suspected gallbladder characteristics, such as the absence of gallbladder stones. Finally, the detective model constructed by retrospective data should be validated by prospective randomized clinical trials. Nonetheless, it is a useful tool for clinical decision-making given a lack of randomized controlled trial data.
In conclusion, our study demonstrates a novel diagnostic nomogram using specific radiological features and clinical factors. It enables clinicians to obtain an individual probability of GBC and may assist clinicians in preoperative decision-making.
CONFLICTS OF INTEREST
Guarantor of the article: Xiujun Cai, MD, PhD, FACS, FRCS.
Specific author contributions: Mingyu Chen, MD, PhD, and Jiasheng Cao, MD, contributed equally to this work. M.Y.C., J.S.C., and X.J.C. in planning and conducting the study; J.S.C., Y.B., C.H.T., and J.L. in collecting and interpreting data; V.J., L.C.B., S.N., S.X.Y, A.P., F.P., A.A., K.K., R.M., and T.B.S. in helping analyze data and all authors in drafting the manuscript, and all authors have approved the final draft submitted.
Financial support: This research was funded by the Opening Fund of Engineering Research Center of Cognitive Healthcare of Zhejiang Province (grant number 2018KFJJ09), a project supported by Scientific Research Fund of Zhejiang Provincial Education Department (Y201941406), and a project supported by Scientific Research Fund of Zhejiang University, which sponsors in the study of collection, analysis, and interpretation of the data and in the writing of the report.
Potential competing interests: None to report.
WHAT IS KNOWN
- ✓ Preoperative decision-making for differentiating gallbladder lesions remains challenging.
WHAT IS NEW HERE
- ✓ This study developed and validated a diagnostic nomogram to identify GBC.
- ✓ Compared with previous methods, this nomogram demonstrated superior sensitivity and accuracy.
- ✓ This nomogram provides useful information to clinicians for presurgery decision-making.
- ✓ The novel diagnostic nomogram enables clinicians to obtain an individual probability of GBC preoperatively and treat them timely to improve patient outcomes.
We thank Tunan Yu, Xu Feng, Yifan Tong, Lian Duan, and Jiliang Shen for efforts spent in checking all image features for the study. We are grateful to Yun Cai, Qihong Lai, and all our colleagues for assistance in this study.
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