A preoperative scoring system to predict lymph node metastasis in intrahepatic cholangiocarcinoma

The optimal number of examined lymph nodes for cancer specific death of intrahepatic cholangiocarcinoma: a population-based study

BACKGROUND

The number of lymph nodes to be removed during tumor resection in patients with intrahepatic cholangiocarcinoma (ICC) has always been a subject of controversy. The correlation between examined lymph nodes (ELN) and cancer-specific mortality (CSM) in individuals with ICC was the purpose of this investigation.

METHODS

Multivariable models were used to analyze data from the Surveillance, Epidemiology, and End Results database on ICC in order to ascertain the connection between ELN count and CSM. Correlation between ELN and cancer-specific survival (CSS) was evaluated by restricted cubic splines (RCS) on a continuous scale. Locally weighted scatterplot smoothing smoother was used to evaluated the hazard ratios (HRs) of ELNs for CSS with the structural breakpoints determined by Chow test.

RESULTS

This investigation incorporated 1335 ICC cases. Independent risk factors for CSM included median household income, race, diagnostic year, tumor grade, clinical stage, pT stage, pN stage, pM stage and ELN count. With the adjustment for covariates, ICC cases showed statistically significant improvements in CSS (HR = 0.88) as the ELN count increased. The best threshold ELN count, as determined by cut-point analysis, was 6, which allowed for accurate CSS probability discrimination.

CONCLUSION

Increasing ELN count indicated better CSS. Our results strongly suggested 6 ELNs as the optimal cut-off number for assessing the standard of lymph node inspection and prognostic classification in ICC.

Should lymphadenectomy be recommended in radical surgery of intrahepatic cholangiocarcinoma patients? A retrospective study

PURPOSE

Intrahepatic cholangiocarcinoma (ICC) is an extremely deadly cancer with high recurrence incidence, particularly in patients with lymph node metastasis (LNM). The necessity of lymphadenectomy including lymph node biology (LNB) and dissection (LND) during ICC radical surgery remains debate.

METHODS

We retrospectively analyzed the patients diagnosed with ICC and underwent radical surgery at the Cancer Hospital of the Chinese Academy of Medical Sciences from 2012 to 2023.

RESULTS

A total of 308 ICC patients were involved in this study. pLNM+ group had poorer OS (P < 0.0001) and poorer DFS (P < 0.0001) compared with pLNM- group. Compared to the LN- group, LN+ group exhibited worse OS (P = 0.038) and worse DFS (P = 0.003). After PSM and IPTW, compared with LN- group, LNB exhibited longer operation time (IPTW: P = 0.0024) and longer hospitalization days (IPTW: P = 0.0112) with no significant differences in complications, DFS, and OS. Compared with LN- group, LND group had no better DFS and OS, only more complications (IPTW: P = 0.0191), longer operation time (all P < 0.001), higher risk of bleeding (all P < 0.05), transfusion (IPTW: P = 0.014) and longer hospitalization days (IPTW: P = 0.0044). Compared with LNB group, LND had longer operation time (P = 0.0227), higher risk of bleeding (P = 0.017) and transfusion (P = 0.0321), and more postoperative complications (P = 0.0425), with no difference in DFS and OS.

CONCLUSION

Lymphadenectomy does not necessarily provide long-term survival or recurrence benefits. LND only achieves the effect of LNB while negatively affects postoperative recovery without survival benefit for ICC patients. LNB can be performed for accurate pathological staging while not all patients may require LND based on their specific circumstances.

To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions

BACKGROUND

This study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized support for clinical decision-making.

METHODS

Clinical, radiological, and pathological data from ICC patients were retrospectively collected. Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. Finally, a lymph node metastasis prediction model was established by integrating the features of several subregional models, and its performance was evaluated.

RESULTS

A total of 164 ICC patients were included in this study, 103 of whom underwent lymph node dissection. The patients were divided into LNM- and LNM + groups on the basis of lymph node status, and significant differences in white blood cell indicators were found between the two groups. Survival analysis revealed that patients with positive lymph nodes had significantly worse prognoses. Through cluster analysis, the optimal number of habitat subregions was determined to be 5, and prediction models based on different subregions were constructed. A comparison of the performance of each model revealed that the Habitat1 and Habitat5 models had excellent performance. The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. Calibration curves and decision curve analysis further validated the superiority and clinical application value of the model.

CONCLUSIONS

This study successfully constructed an accurate prediction model based on habitat subregions that can effectively predict the lymph node metastasis of ICC patients preoperatively. This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.

Association Between MRI-Based Radiomics Features and Regional Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma and Its Clinical Outcome

BACKGROUND

Regional lymph node metastasis (LNM) assessment is crucial for predicting intrahepatic cholangiocarcinoma (iCCA) prognosis. However, imaging assessment has limitations for identifying LNM.

PURPOSE

To investigate the association between MRI radiomics features, regional LNM status, and prognosis in iCCA.

STUDY TYPE

Retrospective.

SUBJECTS

Two hundred ninety-six patients (male = 197) with surgically confirmed iCCA.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T. DWI, T2WI, and contrast-enhanced T1WI.

ASSESSMENT

Clinical information, radiologic, and MRI-based radiomics features associated with LNM status were collected to establish models. Performance of MRI, PET/CT, and the combined LNM models were compared in training (N = 207) and test (N = 89) datasets. Overall survival (OS) was compared based on LNM status.

STATISTICAL TESTS

The independent features were selected by 5-fold cross-validation. The performance of MRI, PET/CT, and the models was evaluated using the area under receiver operating characteristic curve (AUC). Univariable and multivariable Cox regression were used to identify independent variables for OS. Kaplan-Meier curves were compared with the log-rank test between LNM positive and negative groups. P < 0.05 was considered statistically significant.

RESULTS

Intrahepatic duct dilatation, enhancement pattern, and CA19-9 were independent clinicoradiologic features. The radiomics model was constructed by the independent radiomics features extracted from T2WI and delay phase T1WI. The combined LNM model showed AUC of 0.888, 0.884, and 0.811 in training, validation, and test cohorts with a positive net benefit. PET/CT exhibited similar sensitivity to the combined LNM model (0.750 vs. 0.733, P > 0.999) while the combined LNM model showed higher specificity (0.703 vs. 0.630, P = 0.039) in the test cohort. High risk of regional LNM was significantly associated with worse OS (median: 24 months) than low risk (median: 30 months, P < 0.0001).

DATA CONCLUSIONS

The combined LNM model has the strongest correlation with LNM status for mass-forming iCCA patients.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Development and validation of a machine-learning model to predict lymph node metastasis of intrahepatic cholangiocarcinoma: A retrospective cohort study

Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This study involved patients who underwent curative liver resection between different time periods. Three machine learning models were constructed with a training cohort (2010-2016) and validated with a separate cohort (2019-2023). A total of 170 patients were included in the training set and 101 in the validation cohort. The lymph node status of patients not undergoing lymph node dissection was predicted, followed by survival analysis. Among the models, the support vector machine (SVM) had the best discrimination, with an area under the curve (AUC) of 0.705 for the training set and 0.754 for the validation set, compared to the random forest (AUC: 0.780/0.693) and the logistic regression (AUC: 0.703/0.736). Kaplan-Meier analysis indicated that patients in the positive lymph node group or predicted positive group had significantly worse overall survival (OS: p < 0.001 for both) and disease-free survival (DFS: p < 0.001 for both) compared to negative groups. An online user-friendly calculator based on the SVM model has been developed for practical application.

Lymph node metastasis of intrahepatic cholangiocarcinoma: the present and prospect of detection and dissection

Intrahepatic cholangiocarcinoma (ICC) ranks as the second most primary liver cancer that often goes unnoticed with a high mortality rate. Hepatectomy is the main treatment for ICC, but only 15% of patients are suitable for surgery. Despite advancements in therapeutic approaches, ICC has an unfavorable prognosis, largely due to lymph node metastasis (LNM) that is closely linked to the elevated recurrence rates. Consequently, the identification of precise and suitable techniques for the detection and staging of LNM assumes paramount importance for ICC therapy. While preoperative imaging plays a crucial role in ICC diagnosis, its efficacy in accurately diagnosing LNM remains unsatisfactory. The inclusion of lymph node dissection as part of the hepatectomy procedures is significant for the accurate pathological diagnosis of LNM, although it continues to be a topic of debate. The concept of sentinel lymph node in ICC has presented a novel and potentially valuable approach for diagnosing LNM. This review aims to explore the current state and prospects of LNM in ICC, offering a promising avenue for enhancing the clinical diagnosis and treatment of ICC to improve patient prognosis.

Intra- and Peri-tumoral Radiomics Based on Dynamic Contrast Enhanced-MRI to Identify Lymph Node Metastasis and Prognosis in Intrahepatic Cholangiocarcinoma

BACKGROUND

Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM.

PURPOSE

To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA.

STUDY TYPE

Retrospective.

SUBJECTS

Two hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51).

FIELD STRENGTH/SEQUENCE

T1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T.

ASSESSMENT

Radiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated.

STATISTICAL TESTS

Receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant.

RESULTS

The nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS.

DATA CONCLUSION

The nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Probability of Lymph Node Metastases in Patients Undergoing Adequate Lymphadenectomy during Surgery for Intrahepatic Cholangiocarcinoma: A Retrospective Multicenter Study

Introduction: Nodal metastases (lymph node metastasis [LNM]) are one of the major determinants of prognosis following surgery for intrahepatic cholangiocarcinoma (ICC). Previous studies investigating the correlation between clinical-radiological features and the probability of LNM include patients undergoing inadequate nodal sampling. Aim of this study was to develop a model to predict the risk of LNM in patients undergoing adequate lymphadenectomy using preoperative clinical and radiological features. Methods: Patients undergoing radical surgery for ICC with adequate lymphadenectomy at seven Italian Centers between 2000 and 2023 were collected and divided into a derivation and a validation cohort. Logistic regression and dominance analysis were applied in the derivation cohort to identify variables associated with LNM at pathology. The final coefficients were derived from the model having the highest c-statistic in the derivation cohort with the lowest number of variables included (parsimony). The model was then tested in the external validation cohort, and the linear predictor was divided into quartiles to generate four risk categories. Results: A total of 693 patients were identified. Preoperative CA 19-9, clinically suspicious lymph nodes at radiology, patients’ age, and tumor burden score were significantly associated with LNM. These factors were included in a model (<ext-link ext-link-type="uri" xlink:href="https://aicep.website/calculators/" xmlns:xlink="http://www.w3.org/1999/xlink">https://aicep.website/calculators/</ext-link>) showing a c-statistic of 0.723 (95% CI: 0.680, 0.766) and 0.771 (95% CI: 0.699, 0.842) in the derivation and validation cohort, respectively. A progressive increase of pathological lymph node positivity across risk groups was observed (29.9% in low-risk, 45.1% in intermediate-low risk, 51.5% in intermediate-high risk, and 87.3% in high-risk patients; p = 0.001). Conclusions: A novel model that combines preoperative CA 19-9, clinically suspicious lymph nodes at radiology, patients’ age, and tumor burden score was developed to predict the risk of LNM before surgery. The model exhibited high accuracy and has the potential to assist clinicians in the management of patients who are candidate to surgery.

Development and validation of a novel preoperative clinical model for predicting lymph node metastasis in perihilar cholangiocarcinoma

BACKGROUD

We aimed to develop a novel preoperative nomogram to predict lymph node metastasis (LNM) in perihilar cholangiocarcinoma (pCCA) patients.

METHODS

160 pCCA patients were enrolled at Lihuili Hospital from July 2006 to May 2022. A novel nomogram model was established to predict LNM in pCCA patients based on the independent predictive factors selected by the multivariate logistic regression model. The precision of the nomogram model was evaluated through internal and external validation with calibration curve statistics and the concordance index (C-index). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate and determine the clinical utility of the nomogram.

RESULTS

Multivariate logistic regression demonstrated that age (OR = 0.963, 95% CI: 0.930-0.996, P = 0.030), CA19-9 level (> 559.8 U/mL vs. ≤559.8 U/mL: OR = 3.162, 95% CI: 1.519-6.582, P = 0.002) and tumour diameter (OR = 1.388, 95% CI: 1.083-1.778, P = 0.010) were independent predictive factors of LNM in pCCA patients. The C-index was 0.763 (95% CI: 0.667-0.860) and 0.677 (95% CI: 0.580-0.773) in training cohort and validation cohort, respectively. ROC curve analysis indicated the comparative stability and adequate discriminative ability of nomogram. The sensitivity and specificity were 0.820 and 0.652 in training cohort and 0.704 and 0.649 in validation cohort, respectively. DCA revealed that the nomogram model could augment net benefits in the prediction of LNM in pCCA patients.

CONCLUSIONS

The novel prediction model is useful for predicting LNM in pCCA patients and showed adequate discriminative ability and high predictive accuracy.