The diagnostic accuracy of CT and MRI for the detection of lymph node metastases in gallbladder cancer: A systematic review and meta-analysis. Eur J Radiol. 2019;110:156-162. [PMID: 30599854 DOI: 10.1016/j.ejrad.2018.11.034]

Update on the Role of Imaging in the Diagnosis, Staging, and Prognostication of Gallbladder Cancer

Gallbladder cancer (GBC) is a highly aggressive malignancy with dismal prognosis. GBC is characterized by marked geographic predilection. GBC has distinct morphological types that pose unique challenges in diagnosis and differentiation from benign lesions. There are no specific clinical or serological markers of GBC. Imaging plays a key role not only in diagnosis and staging but also in prognostication. Ultrasound (US) is the initial test of choice that allows risk stratification in wall thickening and polypoidal type of gallbladder lesions. US findings guide further investigations and management. Computed tomography (CT) is the test of choice for staging GBC as it allows comprehensive evaluation of the gallbladder lesion, liver involvement, lymph nodes, peritoneum, and other distant sites for potential metastases. Magnetic resonance imaging (MRI) and magnetic resonance cholangiopancreatography allow better delineation of the biliary system involvement. Contrast-enhanced US and advanced MRI techniques including diffusion-weighted imaging and dynamic contrast-enhanced MRI are used as problem-solving tools in cases where distinction from benign lesion is challenging at US and CT. Positron emission tomography is also used in selected cases for accurate staging of the disease. In this review, we provide an up-to-date insight into the role of imaging in diagnosis, staging, and prognostication of GBC.

Gallbladder Cancer

Gallbladder cancer is the most common biliary tract malignancy, often detected incidentally post-cholecystectomy or at an advanced stage, historically linked to a poor prognosis. Advances in minimally invasive surgery and systemic therapies have improved outcomes. Global incidence varies, with risk factors including gender, age, gallbladder disease history, and polyp size influencing malignancy risks. Management involves cross-sectional imaging, staging laparoscopy in select cases, and radical cholecystectomy with lymphadenectomy and adjuvant therapy, though its use is limited. Trials are ongoing assessing the role of neoadjuvant therapy. Prognosis depends on the tumor stage, with early detection crucial for long-term survival.

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. This study aimed to differentiate between XGC and GBC using a deep learning nomogram model built from contrast enhanced computed tomography (CT) scans. 297 patients were included with confirmed XGC (94) and GBC (203) as the training and internal validation cohort from 2017 to 2021. The deep learning model Resnet-18 with Fourier transformation named FCovResnet18, shows most impressive potential in distinguishing XGC from GBC using 3-phase merged images. The accuracy, precision and area under the curve (AUC) of the model were then calculated. An additional cohort of 74 patients consisting of 22 XGC and 52 GBC patients was enrolled from two subsidiary hospitals as the external validation cohort. The accuracy, precision and AUC achieve 0.98, 0.99, 1.00 in the internal validation cohort and 0.89, 0.92, 0.92 in external validation cohort. A nomogram model combining clinical characteristics and deep learning prediction score showed improved predicting value. Altogether, FCovResnet18 nomogram has demonstrated its ability to effectively differentiate XGC from GBC preoperatively, which significantly aid surgeons in making informed and accurate surgical decisions for XGC and GBC patients.

Using machine learning to preoperatively stratify prognosis among patients with gallbladder cancer: a multi-institutional analysis

BACKGROUND

Gallbladder cancer (GBC) is an aggressive malignancy associated with a high risk of recurrence and mortality. We used a machine-based learning approach to stratify patients into distinct prognostic groups using preperative variables.

METHODS

Patients undergoing curative-intent resection of GBC were identified using a multi-institutional database. A classification and regression tree (CART) was used to stratify patients relative to overall survival (OS) based on preoperative clinical factors.

RESULTS

CART analysis identified tumor size, biliary drainage, carbohydrate antigen 19-9 (CA19-9) levels, and neutrophil-lymphocyte ratio (NLR) as the factors most strongly associated with OS. Machine learning cohorted patients into four prognostic groups: Group 1 (n = 109): NLR ≤1.5, CA19-9 ≤20, no drainage, tumor size <5.0 cm; Group 2 (n = 88): NLR >1.5, CA19-9 ≤20, no drainage, tumor size <5.0 cm; Group 3 (n = 46): CA19-9 >20, no drainage, tumor size <5.0 cm; Group 4 (n = 77): tumor size <5.0 cm with drainage OR tumor size ≥5.0 cm. Median OS decreased incrementally with CART group designation (59.5, 27.6, 20.6, and 12.1 months; p < 0.0001).

CONCLUSIONS

A machine-based model was able to stratify GBC patients into four distinct prognostic groups based only on preoperative characteristics. Characterizing patient prognosis with machine learning tools may help physicians provide more patient-centered care.

A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

Objectives

The aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).

Methods

A total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.

Results

The clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.

Conclusion

The findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

Locally advanced gallbladder cancer: a review of the criteria and role of imaging

Gallbladder carcinoma (GBC) is among one of the gastrointestinal malignancies with extremely dismal prognosis. This is due to the advanced stage at presentation. Majority of the patients with GBC are not considered candidates for surgery because of the locally advanced disease or metastases. However, with the accumulating evidence regarding the role of neoadjuvant chemotherapy, there is a need to correctly identify a subset of patients with locally advanced GBC who will benefit maximally from neoadjuvant chemotherapy and will be successfully downstaged to receive curative (R0) surgery. In this context, there is a lack of consensus and different groups have resorted to criteria for locally advanced disease eligible for neoadjuvant chemotherapy based on personal or institutional experiences. Imaging plays a critical role in the evaluation of patients with GBC as it helps stratify patients into resectable and unresectable. Imaging also has the potential to identify patients with locally advanced GBC and hence facilitate neoadjuvant chemotherapy and improve outcomes. In this review, we evaluate the various criteria for locally advanced GBC and the role of imaging in this scenario.

Imaging-based algorithmic approach to gallbladder wall thickening

Gallbladder (GB) wall thickening is a frequent finding caused by a spectrum of conditions. It is observed in many extracholecystic as well as intrinsic GB conditions. GB wall thickening can either be diffuse or focal. Diffuse wall thickening is a secondary occurrence in both extrinsic and intrinsic pathologies of GB, whereas, focal wall thickening is mostly associated with intrinsic GB pathologies. In the absence of specific clinical features, accurate etiological diagnosis can be challenging. The survival rate in GB carcinoma (GBC) can be improved if it is diagnosed at an early stage, especially when the tumor is confined to the wall. The pattern of wall thickening in GBC is often confused with benign diseases, especially chronic cholecystitis, xanthogranulomatous cholecystitis, and adenomyomatosis. Early recognition and differentiation of these conditions can improve the prognosis. In this minireview, the authors describe the patterns of abnormalities on various imaging modalities (conventional as well as advanced) for the diagnosis of GB wall thickening. This paper also illustrates an algorithmic approach for the etiological diagnosis of GB wall thickening and suggests a formatted reporting for GB wall abnormalities.

A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study

BACKGROUND

Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of this study was to identify the risk factors associated with LNM of IGBC and to establish a nomogram to improve the ability to predict the risk of LNM for IGBC.

METHODS

A total of 796 patients diagnosed with stage T1/2 GBC between 2004 and 2015 who underwent surgery and lymph node evaluation were enrolled in this study. We randomly divided the dataset into a training set (70%) and a validation set (30%). A logistic regression model was used to construct the nomogram in the training set and then was verified in the validation set. Nomogram performance was quantified with respect to discrimination and calibration.

RESULTS

The rates of LNM in T1a, T1b and T2 patients were 7, 11.1 and 44.3%, respectively. Tumor diameter, T stage, and tumor differentiation were independent factors affecting LNM. The C-index and AUC of the training set were 0.718 (95% CI, 0.676-0.760) and 0.702 (95% CI, 0.659-0.702), respectively, demonstrating good prediction performance. The calibration curves showed perfect agreement between the nomogram predictions and actual observations. Decision curve analysis showed that the LNM nomogram was clinically useful when the risk was decided at a possibility threshold of 2-63%. The C-index and AUC of the validation set were 0.73 (95% CI: 0.665-0.795) and 0.692 (95% CI: 0.625-0.759), respectively.

CONCLUSION

The nomogram established in this study has good prediction ability. For patients with IGBC requiring re-resection, the model can effectively predict the risk of LNM and make up for the inaccuracy of imaging.

Examination of Prognostic Factors Affecting Long-Term Survival of Patients with Stage 3/4 Gallbladder Cancer without Distant Metastasis

In advanced gallbladder cancer (GBC) radical resection, if multiple prognostic factors are present, the outcome may be poor; however, the details remain unclear. To investigate the poor prognostic factors affecting long-term surgical outcome, we examined 157 cases of resected stage 3/4 GBC without distant metastasis between 1985 and 2017. Poor prognostic factors for overall survival and treatment outcomes of a number of predictable preoperative poor prognostic factors were evaluated. The surgical mortality was 4.5%. In multivariate analysis, blood loss, poor histology, liver invasion, and ≥4 regional lymph node metastases (LNMs) were independent prognostic factors for poor surgical outcomes; invasion of the left margin or the entire area of the hepatoduodenal ligament and a Clavien–Dindo classification ≥3 were marginal factors. The analysis identified outcomes of patients with factors that could be predicted preoperatively, such as liver invasion ≥5 mm, invasion of the left margin or the entire area of the hepatoduodenal ligament, and ≥4 regional LNMs. Thus, the five-year overall survival was 54% for zero factors, 34% for one factor, and 4% for two factors (p < 0.05). A poor surgical outcome was likely when two or more factors were predicted preoperatively; therefore, new treatment strategies are required for such patients.

Lymph Nodal Metastatic Pattern in Carcinoma Gallbladder with Multidetector Computed Tomography: An Institutional Experience

Objective This work aimed to study the distribution of lymph nodal metastatic pattern in carcinoma gallbladder with multidetector computed tomography (CT).

Materials and Methods A retrospective observational study was conducted including 80 patients with carcinoma gallbladder who underwent triple-phase CT angiography or single-phase contrast-enhanced CT scan of the abdomen between January 2019 and November 2019.

Results In our study, 75 (93.7%) out of 80 cases showed metastasis to lymph nodes, with distribution as follows: periportal (69), peripancreatic (62), and aortocaval (47). The most common involved combination included all three lymph nodal groups (periportal, peripancreatic, and aortocaval), involving 40 (50%) cases. The combination of only periportal and peripancreatic lymph nodes was seen in 17 (21%) cases. Isolated periportal lymph nodes were seen in eight cases (10%) cases. The combination of only periportal and aortocaval lymph nodes was seen in four (5%) cases. Isolated peripancreatic lymph nodes were seen in three (3.7%) cases. The combination of periportal and aortocaval was seen in four (5%) cases followed by peripancreatic and aortocaval lymph nodes that was seen in two (2.5%) cases. Isolated aortocaval lymph nodes were seen in one (1.2%) case.

Conclusion Periportal lymph nodes were the single most commonly involved station followed by peripancreatic and aortocaval lymph nodes. The combination of periportal, peripancreatic, and aortocaval lymph nodes was seen most commonly. The second most commonly involved combination was found to be periportal and peripancreatic lymph nodes followed by isolated periportal lymph nodes.

Exosomal miR-182 regulates the effect of RECK on gallbladder cancer

BACKGROUND

As the most common biliary malignancy, gallbladder cancer (GC) is an elderly-biased disease. Although extensive studies have elucidated the molecular mechanism of microRNA 182 (miR-182) and reversion-inducing-cysteine-rich protein with kazal motifs (RECK) in various cancers, the specific role of exosomal miR-182 and RECK in GC remains poorly understood.

AIM

To explore the relationship between exosomal miR-182/RECK and metastasis of GC.

METHODS

Paired GC and adjacent normal tissues were collected from 78 patients. Quantitative polymerase chain reaction was employed to detect miR-182 and exosomal miR-182 expression, and Western blotting was conducted to determine RECK expression. In addition, the effects of exosomal miR-182/RECK on the biological function of human GC cells were observed. Moreover, the double luciferase reporter gene assay was applied to validate the targeting relationship between miR-182 and RECK.

RESULTS

Compared with normal gallbladder epithelial cells, miR-182 was highly expressed in GC cells, while RECK had low expression. Exosomal miR-182 could be absorbed and transferred by cells. Exosomal miR-182 inhibited RECK expression and promoted the migration and invasion of GC cells.

CONCLUSION

Exosomal miR-182 can significantly promote the migration and invasion of GC cells by inhibiting RECK; thus miR-182 can be used as a therapeutic target for GC.

Probing Cancer Metastasis at a Single-Cell Level with a Raman-Functionalized Anionic Probe

Cancer metastasis is the primary reason for cancer-related deaths, yet there is no technique capable of detecting it due to cancer pathogenesis. Current cancer diagnosis methods evaluate tumor samples as a whole/pooled sample process loses heterogeneous information in the metastasis state. Hence, it is not suitable for metastatic cancer detection. In order to gain complete information on metastasis, it is desirable to develop a nondestructive detection method that can evaluate metastatic cells with sensitivity down to single-cell resolution. Here we demonstrated self-functionalized anionic quantum probes for in vitro metastatic cancer detection at a single-cell concentration. We achieved this by incorporating a nondestructive SERS ability within the generated probes by integrating anionic surface species and NIR plasmon resonance. To the best of our knowledge, this was the first time that metastatic cancer cells were detected through their neoplastic transformations. With reliable diagnostic information at the single-cell sensitivity in an in vitro state, we successfully discriminated against cancer malignancy states.

Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

BACKGROUND

We developed a computational model integrating clinical data and imaging features extracted from contrast-enhanced computed tomography (CECT) images, to predict lymph node (LN) metastasis in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

This retrospective study included 159 patients with PDAC (118 in the primary cohort and 41 in the validation cohort) who underwent preoperative contrast-enhanced computed tomography examination between 2012 and 2015. All patients underwent surgery and lymph node status was determined. A total of 2041 radiomics features were extracted from venous phase images in the primary cohort, and optimal features were extracted to construct a radiomics signature. A combined prediction model was built by incorporating the radiomics signature and clinical characteristics selected by using multivariable logistic regression. Clinical prediction models were generated and used to evaluate both cohorts.

RESULTS

Fifteen features were selected for constructing the radiomics signature based on the primary cohort. The combined prediction model for identifying preoperative lymph node metastasis reached a better discrimination power than the clinical prediction model, with an area under the curve of 0.944 vs. 0.666 in the primary cohort, and 0.912 vs. 0.713 in the validation cohort.

CONCLUSIONS

This pilot study demonstrated that a noninvasive radiomics signature extracted from contrast-enhanced computed tomography imaging can be conveniently used for preoperative prediction of lymph node metastasis in patients with PDAC.