Development and validation of a machine learning-based nomogram for prediction of intrahepatic cholangiocarcinoma in patients with intrahepatic lithiasis

The preoperative scoring system combining neutrophil/lymphocyte ratio and CA19-9 predicts the long-term prognosis of intrahepatic cholangiocarcinoma patients undergoing curative liver resection

BACKGROUND

This study aims to investigate preoperative prognostic factors available for intrahepatic cholangiocarcinoma (ICC) patients and propose a new preoperative prognostic scoring system for ICC that combines CA19-9 and neutrophil/lymphocyte ratio (NLR).

METHODS

In this retrospective analysis, 1728 patients diagnosed with ICC and undergoing curative liver resections were studied. This study employed univariate and multivariate Cox regression to find factors affecting recurrence and overall survival (OS), and furthermore assessed how preoperative models influenced tumor traits and postoperative recurrence.

RESULTS

The results of the multivariate Cox regression analysis indicated that two preoperative variables, NLR and Ca19-9, were independent risk factors affecting postoperative recurrence and OS in ICC patients. Based on this data, assigning a score of 0 (NLR ≤ 2.4 and Ca19-9 ≤ 37U/ml) or 1 (NLR > 2.4 and Ca19-9 > 37U/ml) to these two factors, a preoperative prognostic score was derived. According to the scoring model, patients were divided into three groups: 0 points (low-risk group), 1 point (intermediate-risk group), and 2 points (high-risk group). The 5-year recurrence and OS rates for the three groups were 56.6%, 68.2%, 77.8%, and 56.8%, 40.6%, 27.6%, respectively, with all P values < 0.001. Furthermore, high-risk group patients were more prone to early recurrence (early recurrence rates for high-, intermediate-, and low-risk groups were 56.8%, 51.5%, and 37.1%, respectively, P < 0.001) and extrahepatic metastasis (extrahepatic metastasis rates for high-, intermediate-, and low-risk groups were 31.7%, 26.4%, and 15.4%, respectively, P < 0.001). In terms of tumor characteristics, high-risk group patients had larger tumor diameters and were more likely to experience microvascular invasion, lymph node metastasis, and perineural invasion.

CONCLUSIONS

The predictive capacity of postoperative recurrence and OS rates in ICC patients is effectively captured by the preoperative scoring system incorporating NLR and CA19-9 levels.

Establishment and external validation of prognosis prediction nomogram for patients with distant metastatic intrahepatic cholangiocarcinoma: based on a large population

BACKGROUND

Most patients with intrahepatic cholangiocarcinoma (ICC) have developed distant metastasis at the time of diagnosis, while there is rear related nomogram to predict the prognosis.

METHODS

Clinical data of patients pathologically diagnosed of ICC with distant metastasis were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database during 2005 to 2019. Finally, patients diagnosed as ICC in the Second Affiliated Hospital of Nanchang University from 2014 to 2019 were collected for external verification. All data were divided into training cohort and validation cohort in a ratio of 7:3. The nomogram was established based on independent prognostic factors using Cox univariate and multivariate analyses. The area under the receiver operating characteristic (ROC) curves (AUC), the calibration curve and the decision curve analysis (DCA) were used to determine the prediction accuracy of the nomogram.

RESULTS

This study finally included 572 ICC with distant metastasis patients, another 32 patients collected by the author's hospital were used as external verification. Results showed that age, surgery, radiotherapy and chemotherapy were independent prognostic factors, and nomogram was established. The AUC of predicting 3, 6, 9-month overall survival were 0.866, 0.841 and 0.786. The ROC curves and calibration curves showed that the nomogram had good predictive accuracy, and DCA showed that the nomogram had good clinical applicability.

CONCLUSIONS

The nomogram has good accuracy in predicting prognosis of DM-ICC patients, which would be of good significance to improve the prognosis of these patients.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: A multicentre cohort study

PURPOSE

Postoperative early recurrence (ER) leads to a poor prognosis for intrahepatic cholangiocarcinoma (ICC). We aimed to develop machine learning (ML) radiomics models to predict ER in ICC after curative resection.

METHODS

Patients with ICC undergoing curative surgery from three institutions were retrospectively recruited and assigned to training and external validation cohorts. Preoperative arterial and venous phase contrast-enhanced computed tomography (CECT) images were acquired and segmented. Radiomics features were extracted and ranked through their importance. Univariate and multivariate logistic regression analysis was used to identify clinical characteristics. Various ML algorithms were used to construct radiomics-based models, and the predictive performance was evaluated by receiver operating characteristic curves, calibration curves, and decision curve analysis.

RESULTS

127 patients were included for analysis: 90 patients in the training set and 37 patients in the validation set. Ninety-two patients (72.4%) experienced recurrence, including 71 patients exhibiting ER. Male sex, microvascular invasion, TNM stage, and serum CA19-9 were identified as independent risk factors for ER, with the corresponding clinical model having a poor predictive performance (AUC of 0.685). Fifty-seven differential radiomics features were identified, and the 10 most important features were utilized for modelling. Seven ML radiomics models were developed with a mean AUC of 0.87 ± 0.02, higher than the clinical model. Furthermore, the clinical-radiomics models showed similar predictive performance to the radiomics models (AUC of 0.87 ± 0.03).

CONCLUSION

ML radiomics models based on CECT are valuable in predicting ER in ICC.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

BACKGROUND

This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma.

METHODS

The PubMed database was searched for papers published in the English language no earlier than October 2022.

RESULTS

We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective.

CONCLUSIONS

It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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.

Predictors of Distant Metastasis and Prognosis in Newly Diagnosed T1 Intrahepatic Cholangiocarcinoma

Background

According to American Joint Committee on Cancer (AJCC) 8th staging system, T1 intrahepatic cholangiocarcinoma (T1 ICC) is considered a tumor with no vascular invasion. However, T1 ICC usually occurs distant metastasis (DM), and the clinical features of these patients could help clinicians identify the high-risk population.

Methods

We reviewed 1959 newly diagnosed patients with T1 ICC from the Surveillance, Epidemiology, and End Results (SEER) database during 2004-2018. Logistic regression models and Cox proportional hazards models were conducted to predict the risk of DM and overall survival (OS), respectively, and then, web-based nomograms were constructed. Decision curve analysis (DCA) and clinical impact curves (CIC) were used to measure the clinical utility of the models. The low-, medium-, and high-risk groups were identified by calculating the summary of the risk points. Nomograms on the web were also created to help clinicians better use these prediction models.

Results

Tumor size and lymph node metastasis accounted for the first two largest proportions among the DM nomogram scores, while surgery, DM, age at diagnosis, chemotherapy, and lymph node metastasis occupied the largest percentage in OS nomogram. DM nomogram was established for these newly diagnosed patients with T1 ICC, and OS nomogram was developed to visually predict the OS rate of 3, 5, and 10 years. The calibration curves revealed a valid predictive accuracy of nomograms, of which the C-index was 0.703 and 0.740, respectively, for good discrimination. DCAs, CICs, and risk subgroups showed the clinical validity of these nomograms. Two websites were created to make it easier to use these nomograms.

Conclusions

Novel web-based nomograms predicting the risk of DM and OS for T1 ICC were constructed. These predictive tools might help clinicians make precise clinical strategies for each patient with T1 ICC.

Exploring the function of stromal cells in cholangiocarcinoma by three-dimensional bioprinting immune microenvironment model

The tumor immune microenvironment significantly affects tumor progression, metastasis, and clinical therapy. Its basic cell components include tumor-associated endothelial cells, fibroblasts, and macrophages, all of which constitute the tumor stroma and microvascular network. However, the functions of tumor stromal cells have not yet been fully elucidated. The three-dimensional (3D) model created by 3D bioprinting is an efficient way to illustrate cellular interactions in vitro. However, 3D bioprinted model has not been used to explore the effects of stromal cells on cholangiocarcinoma cells. In this study, we fabricated 3D bioprinted models with tumor cells and stromal cells. Compared with cells cultured in two-dimensional (2D) environment, cells in 3D bioprinted models exhibited better proliferation, higher expression of tumor-related genes, and drug resistance. The existence of stromal cells promoted tumor cell activity in 3D models. Our study shows that 3D bioprinting of an immune microenvironment is an effective way to study the effects of stromal cells on cholangiocarcinoma cells.

Artificial intelligence in prostate cancer: Definitions, current research, and future directions

Multiple novel modalities tasking artificial intelligence based computational pathology applications and integrating other variables, such as risk factors, tumor microenvironment, genomic testing data, laboratory findings, clinical history, and radiology findings, will improve diagnostic consistency and generate a synergistic diagnostic workflow. In this article, we present the concise and contemporary review on the utilization of artificial intelligence in prostate cancer and identify areas for possible future applications.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.