Comparison of oncologic outcomes of extrahepatic cholangiocarcinoma according to tumor location: perihilar cholangiocarcinoma versus distal bile duct cancer

Design of the distribution of iron oxide (Fe3O4) nano-particle drug in realistic cholangiocarcinoma model and the simulation of temperature increase during magnetic induction hyperthermia

The prognosis for Cholangiocarcinoma (CCA) is unfavorable, necessitating the development of new therapeutic approach such as magnetic hyperthermia therapy (MHT) which is induced by magnetic nano-particle (MNPs) drug to bridge the treatment gap. Given the deep location of CCA within the abdominal cavity and proximity to vital organs, accurately predict the individualized treatment effects and safety brought by the distribution of MNPs in tumor will be crucial for the advancement of MHT in CCA. The Mimics software was used in this study to conduct three-dimensional reconstruction of abdominal computed tomography (CT) and magnetic reso-nance imaging images from clinical patients, resulting in the generation of a realistic digital geometric model representing the human biliary tract and its adjacent structures. Subsequently, The COMSOL Multiphysics software was utilized for modeling CCA and calculating the heat transfer law resulting from the multi-regional distribution of MNPs in CCA. The temperature within the central region of irregular CCA measured approximately 46°C, and most areas within the tumor displayed temperatures surpassing 41°C. The temperature of the inner edge of CCA is only 39 ~ 41℃, however, it can be ameliorated by adjusting the local drug concentration through simulation system. For CCA with diverse morphologies and anatomical locations, the multi-regional distribution patterns of intratumoral MNPs and a slight overlap of drug distribution areas synergistically enhance intratumoral temperature while ensuring treatment safety. The present study highlights the practicality and imperative of incorporating personalized intratumoral MNPs distribution strategy into clinical practice for MHT, which can be achieved through the development of an integrated simulation system which incorporates medical image data and numerical calculations.

Clinical Features and Prognostic Models in Patients with Intrahepatic Cholangiocarcinoma: a Population-Based Analysis

BACKGROUND

This study aims to construct a risk classification system and a nomogram in intrahepatic cholangiocarcinomafor patients (ICC).

METHODS

Three thousand seven hundred thirty-seven patients diagnosed with ICC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results. The consistency index, time-dependent receiver operating characteristic curve, and the calibration plots were adopted to evaluate the effective performance of nomogram. Decision curve analysis (DCA), net reclassification index (NRI), and comprehensive discrimination improvement (IDI) were used to compare the advantages and disadvantages of two models. Kaplan-Meier curve showed the difference in prognosis among different groups.

RESULTS

Ten variables were selected to establish the nomogram for ICCA. The C-index (training cohort: 0.765, P < 0.05; validation cohort: 0.776, P < 0.05) and the time-dependent AUCs (the training cohort: the values of 1, 3, 5 years were 0.836, 0.873, and 0.888; the validation cohort: the values of 1, 3, 5 years were 0.833, 0.838, and 0.881) showed satisfactory discrimination. The calibration curves also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 3-, 5-year CSS: 0.879, 0.94, 0.771; validation cohort: 1-, 3-, 5-year CSS: 0.905, 0.945, 0.717) and IDI (training cohort: 1-, 3-, 5-year CSS: 0.24, 0.23, 0.22; validation cohort: 1-, 3-, 5-year CSS: 0.24, 0.46, 0.27) (P < 0.05) (compared with AJCC staging). DCA showed that the new model was more practical and had better recognition than AJCC staging.

CONCLUSIONS

A new risk stratification system for ICC patients has been developed, which can be a practical tool for patient management.

Establishment and validation of a prognostic nomogram for extrahepatic cholangiocarcinoma

Simple summary

Accurately estimate the prognosis of patients with ECCA is important. However, the TNM system has some limitations, such as low accuracy, exclusion of other factors (e.g., age and sex), and poor performance in predicting individual survival risk. In contrast, a nomogram-based clinical model related to a comprehensive analysis of all risk factors is intuitive and straightforward, facilitating the probabilistic analysis of tumor-related risk factors. Simultaneously, a nomogram can also effectively drive personalized medicine and facilitate clinicians for prognosis prediction. Therefore, we construct a novel practical nomogram and risk stratification system to predict CSS in patients with ECCA.

Background

Accurately estimate the prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) was important, but the existing staging system has limitations. The present study aimed to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in ECCA patients.

Methods

3415 patients diagnosed with ECCA between 2010 and 2015 were selected from the SEER database and randomized into a training cohort and a validation cohort at 7:3. The nomogram was identified and calibrated using the C-index, receiver operating characteristic curve (ROC), and calibration plots. Decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI) and the risk stratification were used to compare the nomogram with the AJCC staging system.

Results

Nine variables were selected to establish the nomogram. The C-index (training cohort:0.785; validation cohort:0.776) and time-dependent AUC (>0.7) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 2-, and 3-year CSS:0.27, 0.27,0.52; validation cohort:1-,2-,3-year CSS:0.48,0.13,0.34), IDI (training cohort: 1-, 2-, 3-year CSS:0.22,0.18,0.16; validation cohort: 1-,2-,3-year CSS:0.18,0.16,0.17), and DCA indicated that the established nomogram significantly outperformed the AJCC staging system (P<0.05) and had better recognition compared to the AJCC staging system.

Conclusions

We developed a practical prognostic nomogram to help clinicians assess the prognosis of patients with ECCA.