Quality and performance of validated prognostic models for survival after resection of intrahepatic cholangiocarcinoma: a systematic review and meta-analysis

Efficacy and safety of anlotinib plus anti-PD-1 agents in patients with refractory advanced biliary tract cancers

BACKGROUND

Anlotinib has demonstrated promising anti-tumor efficacy in various solid tumors. Additionally, there is evidence suggesting that immune therapy can enhance the systemic responses of anlotinib. This study aimed to assess the effectiveness and safety of combining anlotinib with PD-1 inhibitors compared to fluoropyrimidine-based chemotherapy as a second-line treatment option for advanced biliary tract cancers (BTCs).

METHODS

A total of 242 patients with BTCs were screened at the First Affiliated Hospital of Zhengzhou University from October 2015 to October 2022. Among them, 78 patients who received either anlotinib plus PD-1 inhibitors (AP) or fluoropyrimidine-based chemotherapy (FB) as second-line treatment were included in the study. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included objective response rate (ORR), disease control rate (DCR), overall survival (OS), safety, and predictive tumor biomarkers.

RESULTS

Among the 78 patients with BTCs, 39 patients received AP, while 39 patients were administered FB. The ORR in the AP group was 20.5%, compared to 5.1% in the FB group. The DCR was 87.2% in the AP group and 66.7% in the FB group. The AP group demonstrated significantly better ORR and DCR compared to the FB group (p = 0.042, p = 0.032). The median PFS and OS in the AP group were 7.9 months (95% CI: 4.35-11.45) and 13.9 months (95% CI: 5.39-22.41), respectively. In the FB group, the median PFS and OS were 4.1 months (95% CI: 3.17-5.03) and 13.2 months (95% CI: 8.72-17.68), respectively. The AP group exhibited significantly better median PFS than the FB group (p = 0.027). In the subgroup analysis, patients without liver metastasis had a much longer PFS in the AP group compared to the FB group (14.3 vs. 5.5 months, p = 0.016). Similarly, patients with CEA ≤ 5 μg/L also demonstrated a longer PFS in the AP group compared to the FB group (8.7 vs. 3.9 months, p = 0.008).

CONCLUSIONS

The combination of anlotinib and PD-1 inhibitors demonstrated a promising clinical effect compared to fluoropyrimidine-based chemotherapy in the second-line treatment of refractory advanced BTCs. Liver metastases and CEA levels may serve as predictive factors for identifying patients who may benefit from AP therapy.

Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately determine patient prognosis, aiming to develop reference values to guide physicians in developing more effective treatment plans.

METHODS

This study used machine learning (ML) algorithms to build prediction models using ICC data on 1,751 patients from the SEER (Surveillance, Epidemiology, and End Results) database and 58 hospital cases. The models' performances were compared using receiver operating characteristic curve analysis, C-index, and Brier scores.

RESULTS

A total of eight variables were used to construct the ML models. Our analysis identified the random survival forest model as the best for prognostic prediction. In the training cohort, its C-index, Brier score, and Area Under the Curve values were 0.76, 0.124, and 0.882, respectively, and it also performed well in the test cohort. Kaplan-Meier survival analysis revealed that the model could effectively determine patient prognosis.

CONCLUSIONS

To our knowledge, this is the first study to develop ML prognostic models for ICC in the high-incidence age group. Of the ML models, the random survival forest model was best at prognosis prediction.

Development and validation of the Baseline Recurrence Risk in Cellulitis (BRRISC) score

OBJECTIVES

Cellulitis is often treated with antibiotics for longer than recommended by guidelines. Prolonged therapy may reduce recurrence in certain patients, but it is not known which patients are at greatest risk. Our objective was to develop and temporally validate a risk prediction score to identify patients attending hospital with cellulitis at highest risk of recurrence.

METHODS

We included UK adult patients with cellulitis attending hospital in an electronic health records (EHR) study to identify demographic, comorbid, physiological, and laboratory factors predicting recurrence (before death) within 90 days, using multivariable logistic regression with backwards elimination in complete cases. A points-based risk score integerised model coefficients for selected predictors. Performance was assessed using the C-index in development and temporal validation samples.

RESULTS

The final model included 4938 patients treated for median 8 days (IQR 6-11); 8.8% (n = 436) experienced hospitalisation-associated recurrence. A risk score using eight variables (age, heart rate, urea, platelets, albumin, previous cellulitis, venous insufficiency, and liver disease) ranged from 0-15, with C-index = 0.65 (95%CI: 0.63-0.68). Categorising as low (score 0-1), medium (2-5) and high (6-15) risk, recurrence increased fourfold; 3.2% (95%CI: 2.3-4.4%), 9.7% (8.7-10.8%), and 16.6% (13.3-20.4%). Performance was maintained in the validation sample (C-index = 0.63 (95%CI: 0.58-0.67)). Among patients at high risk, four distinct clinical phenotypes were identified using hierarchical clustering 1) young, acutely unwell with liver disease; 2) comorbid with previous cellulitis and venous insufficiency; 3) chronic renal disease with severe renal impairment; and 4) acute severe illness, with substantial inflammatory responses.

CONCLUSIONS

Risk of cellulitis recurrence varies markedly according to individual patient factors captured in the Baseline Recurrence Risk in Cellulitis (BRRISC) score. Further work is needed to optimise the score, considering baseline and treatment response variables not captured in EHR data, and establish the utility of risk-based approaches to guide optimal antibiotic duration.

Identification of FOXP1 as a favorable prognostic biomarker and tumor suppressor in intrahepatic cholangiocarcinoma

BACKGROUND

Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported.

METHODS

Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells.

RESULTS

FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues (p < 0.01). The positive rates of FOXP1 were significantly lower in patients with poor differentiation, lymph node metastasis, invasion into surrounding organs, and advanced stages (p < 0.05). Notably, patients with FOXP1 positivity had better outcomes (overall survival) than those with FOXP1 negativity (p < 0.05), as revealed by Kaplan-Meier survival analysis. Moreover, Cox multivariate analysis showed that negative FOXP1 expression, advanced TNM stages, invasion, and lymph node metastasis were independent prognostic risk factors in patients with ICC. Lastly, overexpression of FOXP1 inhibited the proliferation, migration, and invasion of ICC cells and promoted apoptosis, whereas knockdown of FOXP1 had the opposite role.

CONCLUSION

Our findings suggest that FOXP1 may serve as a novel outcome predictor for ICC as well as a tumor suppressor that may contribute to cancer treatment.

Lymph Node Dissection in Intrahepatic Cholangiocarcinoma: a Critical and Updated Review of the Literature

BACKGROUND

Lymphatic spread of intrahepatic cholangiocarcinoma (iCCA) is common and negatively impacts survival. However, the precise role of lymph node dissection (LND) in oncologic outcomes for patients with intrahepatic cholangiocarcinoma remains to be established.

METHODS

Updated evidence on the preoperative diagnosis and prognostic value of lymph node metastasis is reviewed, as well as the potential benefit of LND in patients with iCCA.

RESULTS

The ability to accurately determine nodal status for iCCA with current imaging modalities is equivocal. LND has prognostic value for both survival and disease recurrence. However, execution rates of LND are highly varied in the literature, ranging from 26.9 to 100%. At least 6 lymph nodes should be examined from nodal stations of the hepatoduodenal ligament and hepatic artery as well as based on the location of the primary tumor. Neoadjuvant therapies may be beneficial if lymph node metastases at diagnosis are suspected. Surgeons performing a minimally invasive approach should focus on increasing LND rates and harvesting ≥ 6 lymph nodes. Lymph node negativity is required in patients with iCCA being considered for liver transplantation under investigational protocols.

CONCLUSION

Despite an upward trend in the LND rate, the reality is that only 10% of patients with iCCA receive an adequate LND. This review underscores the importance of routinely increasing the rate of adequate LND in these patients in order to achieve accurate staging, appropriately select patients for adjuvant therapy, and improve the prognosis of clinical outcomes. While prospective data is lacking, the therapeutic impact of LND remains unknown.

Accounting for time dependency in meta-analyses of concordance probability estimates

Recent years have seen the development of many novel scoring tools for disease prognosis and prediction. To become accepted for use in clinical applications, these tools have to be validated on external data. In practice, validation is often hampered by logistical issues, resulting in multiple small-sized validation studies. It is therefore necessary to synthesize the results of these studies using techniques for meta-analysis. Here we consider strategies for meta analyzing the concordance probability for time-to-event data ("C-index"), which has become a popular tool to evaluate the discriminatory power of prediction models with a right-censored outcome. We show that standard meta-analysis of the C-index may lead to biased results, as the magnitude of the concordance probability depends on the length of the time interval used for evaluation (defined e.g., by the follow-up time, which might differ considerably between studies). To address this issue, we propose a set of methods for random-effects meta-regression that incorporate time directly as covariate in the model equation. In addition to analyzing nonlinear time trends via fractional polynomial, spline, and exponential decay models, we provide recommendations on suitable transformations of the C-index before meta-regression. Our results suggest that the C-index is best meta-analyzed using fractional polynomial meta-regression with logit-transformed C-index values. Classical random-effects meta-analysis (not considering time as covariate) is demonstrated to be a suitable alternative when follow-up times are small. Our findings have implications for the reporting of C-index values in future studies, which should include information on the length of the time interval underlying the calculations.

Translating imaging traits of mass-forming intrahepatic cholangiocarcinoma into the clinic: From prognostic to therapeutic insights

Background & Aims

The progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour underlying imaging traits. We developed and validated imaging-based prognostic systems for resected MICCs with an appraisal of the tumour immune microenvironment (TIME) underpinning patient-specific imaging traits.

Methods

Between January 2009 and December 2019, a total of 322 patients who underwent dynamic computed tomography/magnetic resonance imaging and curative-intent resection for MICC at three hepatobiliary institutions were retrospectively recruited, divided into training (n = 193) and validation (n = 129) datasets. Two radiological and clinical scoring (RACS) systems, one integrating preoperative variables and one integrating preoperative and postoperative variables, were developed using Cox regression analysis. We then prospectively analysed the TIME of tissue samples from 20 patients who met study criteria from January 2021 to December 2021 using multiplexed immunofluorescence.

Results

Preoperative and postoperative MICC-RACS systems built on carbohydrate antigen 19-9, albumin, tumour number, radiological/pathological nodal status, pathological necrosis, and three radiological traits (arterial enhancement pattern, tumour boundary, and capsular retraction) demonstrated good performance in predicting disease-specific (C-statistic >0.80) and disease-free (C-statistic >0.75) survival that outperformed rival models and staging systems across study cohorts (P <0.05 for all). Patients with MICC-RACS score of 0-2 (low risk), 3-5 (medium risk), and ≥6 (high risk) had incrementally worse prognosis after surgery. Significant differences in spatial distribution and infiltration level of immune cells were identified between arterial enhancement patterns. Enhanced infiltration of immunosuppressive regulatory T cells and M2-like macrophages at the invasive margin were noted in tumours with distinct boundary and capsular retraction, respectively.

Conclusions

Our MICC-RACS systems are simple but powerful prognostic tools that may facilitate the understanding of spatially distinct TIMEs and patient-tailored immunotherapy approach.

Impact and Implications

The progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour of MICC underlying imaging traits. In this study, we proposed novel and easy-to-use tools, built on radiological and clinical features, that demonstrated good performance in predicting the prognosis either before or after surgery and outperformed rival models/systems across major imaging modalities. The characteristic radiological traits integrated into prognostic systems (arterial enhancement pattern, tumour boundary, and capsular retraction) were highly correlated with heterogeneous tumour-immune microenvironments, thereby renovating treatment paradigms for this difficult-to-treat disease.

Surgical Management of Intrahepatic Cholangiocarcinoma: Quo Vadis

Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver malignancy related to very high mortality rates [...].

Call to Improve the Quality of Prediction Tools for Intrahepatic Cholangiocarcinoma Resection: A Critical Appraisal, Systematic Review, and External Validation Study

Objective

To conduct a systematic review, critical appraisal, and external validation of survival prediction tools for patients undergoing intrahepatic cholangiocarcinoma (iCCA) resection.

Summary background data

Despite the development of several survival prediction tools in recent years for patients undergoing iCCA resections, there is a lack of critical appraisal and external validation of these models.

Methods

We conducted a systematic review and critical appraisal of survival and recurrence prediction models for patients undergoing curative-intent iCCA resections. Studies were evaluated based on their model design, risk of bias, reporting, performance, and validation results. We identified the best model and externally validated it using our institution's data.

Results

This review included a total of 31 studies, consisting of 26 studies with original prediction tools and 5 studies that only conducted external validations. Among the 26, 54% of the studies conducted internal validations, 46% conducted external validations, and only 1 study scored a low risk of bias. Harrell's C-statistics ranged from 0.67 to 0.76 for internal validation and from 0.64 to 0.75 for external validation. Only 81% of the studies reported model calibration. Our external validation of the best model (Intrahepatic Cholangiocarcinoma [ICC]-Metroticket) estimated Harrell's and Uno's C-statistics of 0.67 (95% CI: 0.56-0.77) and Uno's time-dependent area under the receiver operating characteristic curve (AUC) of 0.71 (95% CI: 0.53-0.88), with a Brier score of 0.20 (95% CI: 0.15-0.26) and good calibration plots.

Conclusions

Many prediction models have been published in recent years, but their quality remains poor, and minimal methodological quality improvement has been observed. The ICC-Metroticket was selected as the best model (Uno's time-dependent AUC of 0.71) for 5-year overall survival prediction in patients undergoing curative-intent iCCA resection.

Long-Term Recurrence-Free and Overall Survival Differ Based on Common, Proliferative, and Inflammatory Subtypes After Resection of Intrahepatic Cholangiocarcinoma

INTRODUCTION

While generally associated with poor prognosis, intrahepatic cholangiocarcinoma (ICC) can have a heterogeneous presentation and natural history. We sought to identify specific ICC subtypes that may be associated with varied long-term outcomes and patterns of recurrence after liver resection.

METHODS

Patients who underwent curative-intent resection for ICC from 2000 to 2020 were identified from a multi-institutional database. Hierarchical cluster analysis characterized three ICC subtypes based on morphology (i.e., tumor burden score [TBS]) and biology (i.e., preoperative neutrophil-to-lymphocyte ratio [NLR] and CA19-9 levels).

RESULTS

Among 598 patients, the cluster analysis identified three ICC subtypes: Common (n = 300, 50.2%) (median, TBS: 4.5; NLR: 2.4; CA19-9: 38.0 U/mL); Proliferative (n = 246, 41.1%) (median, TBS: 8.8; NLR: 2.9; CA19-9: 71.2 U/mL); Inflammatory (n = 52, 8.7%) (median, TBS: 5.4; NLR: 12.6; CA19-9: 26.7 U/mL). Median overall survival (OS) (Common: 72.0 months; Proliferative: 31.4 months; Inflammatory: 22.9 months) and recurrence-free survival (RFS) (Common: 21.5 months; Proliferative: 11.9 months; Inflammatory: 9.0 months) varied considerably among the different ICC subtypes (all p < 0.001). Even though patients with Inflammatory ICC had more favorable T-(T1/T2, Common: 84.4%; Proliferative: 80.6%; Inflammatory: 86.5%) and N-(N0, Common: 14.0%; Proliferative: 20.7%; Inflammatory: 26.9%) disease, the Inflammatory subtype was associated with a higher incidence of intra- and extrahepatic recurrence (Common: 15.8%; Proliferative: 24.2%; Inflammatory: 28.6%) (all p = 0.01).

CONCLUSIONS

Cluster analysis identified three distinct subtypes of ICC based on TBS, NLR, and CA19-9. ICC subtype was associated with RFS and OS and predicted worse outcomes among patients. Despite more favorable T- and N-disease, the Inflammatory ICC subtype was associated with worse outcomes ICC subtype should be considered in the prognostic stratification of patients.

Predictive risk-score model to select patients with intrahepatic cholangiocarcinoma for adjuvant chemotherapy

BACKGROUND

The aim of this study was to develop a predictive model to identify individuals most likely to derive overall survival (OS) benefit from adjuvant chemotherapy (AC) after hepatic resection of intrahepatic cholangiocarcinoma (ICC).

METHODS

Patients who underwent hepatic resection of ICC between 1990 and 2020 were identified from a multi-institutional database. Factors associated with worse OS were identified and incorporated into an online predictive model to identify patients most likely to benefit from AC.

RESULTS

Among 726 patients, 189 (26.0%) individuals received AC. Factors associated with OS on multivariable analysis included CA19-9 (Hazard Ratio [HR]1.17, 95%CI 1.04-1.31), tumor burden score (HR1.09, 95%CI 1.04-1.15), T-category (T2/3/4, HR1.73, 95%CI 1.73-2.64), nodal disease (N1, HR3.80, 95%CI 2.02-7.15), tumor grade (HR1.88, 95%CI 1.00-3.55), and morphological subtype (HR2.19, 95%CI 1.08-4.46). A weighted predictive score was devised and made available online (https://yutaka-endo.shinyapps.io/ICCrisk_model_for_AC/). Receipt of AC was associated with a survival benefit among patients at high/medium-risk (high: no AC, 0% vs. AC, 20.6%; medium: no AC, 36.4% vs. 40.8%; both p < 0.05) but not low-risk (low: no AC, 65.1% vs. AC, 65.1%; p = 0.73) tumors.

CONCLUSION

An online predictive model based on tumor characteristics may help identify which patients may benefit the most from AC following resection of ICC.

Molecular diagnostics and biomarkers in cholangiocarcinoma

Regardless of anatomic origin, cholangiocarcinoma is generally an aggressive malignancy with a relatively high case fatality. Surgical resection with curative intent remains the best opportunity to achieve meaningful long-term survival. Most patients present, however, with advanced disease and less than 20% of patients are candidates for surgical resection. Unfortunately, even patients who undergo resection have a 5-year survival that ranges from 20 to 40%. Biomarkers are indicators of normal, pathologic, or biologic responses to an intervention and can range from a characteristic (i.e., blood pressure reading which can detect hypertension) to specific genetic mutations or proteins (i.e., carcinoembryonic antigen level). Novel biomarkers and improved molecular diagnostics represent an attractive opportunity to improve detection as well as to identify novel therapeutic targets for patients with cholangiocarcinoma. We herein review the latest advances in molecular diagnostics and biomarkers related to the early detection and treatment of patients with cholangiocarcinoma.

Incidence Trend and Competing Risk Analysis of Patients With Intrahepatic Cholangiocarcinoma: A Population-Based Study

Background

Intrahepatic cholangiocarcinoma (ICCA) is a primary liver cancer characterized by rapid progression and poor prognosis. There are few effective tools for evaluating the prognosis of ICCA patients, and the use of liver transplantation (LT) of the treatment for ICCA is still controversial.

Methods

We analyzed ICCA incidence data and clinicopathological data from the Surveillance, Epidemiology, and End Results database. Prognostic predictors were identified by univariate and multivariate Cox regression analyses and then used to establish a nomogram. The prediction performance of the nomogram was evaluated with receiver operating characteristic (ROC) curves, calibration plots and decision curve analysis (DCA) plots. Propensity score matching (PSM) was used to balance the baseline data of patients undergoing LT and other operations, and then, univariate Cox regression analysis was used to evaluate the therapeutic value of LT for ICCA.

Results

The incidence of ICCA increased significantly, from 0.6 per 100,000 in 2,000 to 1.3 per 100,000 in 2018. The median overall survival (OS) of the patients was 13 months, and the 1-, 3-, and 5-year OS rates were 51.40, 22.14, and 13.79%, respectively. Cox regression analysis showed that age under 60 years old, female, tumor size ≤ 50 mm, better differentiation, smaller range of tumor invasion, lack of distant metastasis, regional lymph node surgery and treatment were associated with a better prognosis. The ROC curves, calibration plots, and DCA plots showed that the nomogram had good discrimination and calibration power, as well as clinical utility. After PSM, the univariate Cox regression analysis showed no significant difference in OS between patients treated with LT and patients treated with other operations.

Conclusion

The incidence of ICCA increased significantly. A nomogram with good predictive performance was developed to predict the OS of ICCA patients. LT might be considered as a potential option for some ICCA patients.

Development and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma

Background

Accurate prognosis assessment is essential for surgically resected intrahepatic cholangiocarcinoma (ICC) while published prognostic tools are limited by modest performance. We therefore aimed to establish a novel model to predict survival in resected ICC based on readily-available clinical parameters using machine learning technique.

Methods

A gradient boosting machine (GBM) was trained and validated to predict the likelihood of cancer-specific survival (CSS) on data from a Chinese hospital-based database using nested cross-validation, and then tested on the Surveillance, Epidemiology, and End Results (SEER) database. The performance of GBM model was compared with that of proposed prognostic score and staging system.

Results

A total of 1050 ICC patients (401 from China and 649 from SEER) treated with resection were included. Seven covariates were identified and entered into the GBM model: age, tumor size, tumor number, vascular invasion, number of regional lymph node metastasis, histological grade, and type of surgery. The GBM model predicted CSS with C-Statistics ≥ 0.72 and outperformed proposed prognostic score or system across study cohorts, even in sub-cohort with missing data. Calibration plots of predicted probabilities against observed survival rates indicated excellent concordance. Decision curve analysis demonstrated that the model had high clinical utility. The GBM model was able to stratify 5-year CSS ranging from over 54% in low-risk subset to 0% in high-risk subset.

Conclusions

We trained and validated a GBM model that allows a more accurate estimation of patient survival after resection compared with other prognostic indices. Such a model is readily integrated into a decision-support electronic health record system, and may improve therapeutic strategies for patients with resected ICC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09352-3.

Prognostic biomarkers for cholangiocarcinoma (CCA): state of the art

Introduction:Although advances in understanding the molecular basis of cholangiocarcinoma (CCA) have been made, surgery is the only curative therapy option and the overall prognosis of patients suffering from the disease remains poor. Therefore, estimation of prognosis based on known and novel biomarkers is essential for therapy guidance of CCA in both, curative and palliative settings.Areas covered:An extensive literature search on biomarkers for CCA with special emphasis on prognosis was performed. Based on this, prognostic biomarkers from serum, tumor tissue and other compartments that are currently in use or under evaluation for CCA were summarized in this review. Furthermore, an overview of new biomarkers was provided including those determined from extracellular vesicles (EVs), metabolites and nucleic acids. Finally, prognostic markers associated with potential new therapy options for the treatment of CCA were summed up.Expert opinion:So far, an optimal prognostic biomarker for CCA has not been described. However, based on the increasing knowledge about the molecular basis of CCA but also due to novel, innovative technologies, a plethora of novel prognostic biomarkers is currently under evaluation and will be available for CCA in future.