Strategy for improving the prognosis of patients with intrahepatic cholangiocarcinoma by surgical treatment : Considerations based on experience and a literature review

Tashiro, Seiki; Tsuji, Tatsuya; Miyake, Hidenori; Kamo, Hitomi; Sumise, Yuko; Takai, Shigeharu; Yoshioka, Kazuo

doi:10.2152/jmi.68.15

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Current data support that it has rising morbidity and mortality, difficulty in the early diagnosis, and unsatisfied therapeutic outcome, resulting in a poor prognosis of this cancer that has attracted the special attention of the medical community ( 1 , 26 , 27 ). Its overall 5-year survival is estimated to be less than one-third in patients undergoing radical surgery ( 28 ). The etiology of CCA is related to a strong genetic heterogeneity, and there is an absence of comprehensive cognition of the pathogenesis of CCA at present ( 29 , 30 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

et al. 2021

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Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

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Section: Discussionmentioning

confidence: 99%

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

et al. 2021

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“…However, many patients are found to be in the advanced stage and lose the opportunity to undergo the radical surgery. In addition, after iCCA patients undergoing radical surgery, the five-year survival still remains poor, less than one-third [ 3 ]. Achieving optimal outcomes depends on a skilled, multidisciplinary team that is experienced with the management of advanced biliary disease [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

Zhou

Jv²,

Meng

et al. 2022

Annals of Medicine

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Background and aims Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. Methods 4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation based on machine learning. All the models were trained using the open-source Python library scikit-survival version 0.16.0. Shapley additive explanations method was used to help clinicians better understand the obtained results. Permutation importance was calculated using library ELI5. Results All involved treatment modalities could contribute to a better prognosis. Three models were derived and tested using different data sources, with concordance indices of 0.67, 0.69, and 0.73, respectively. The prediction results were consistent with those under actual situations involving randomly selected patients. Model 2, trained using the hospital data, was selected to develop an online tool, due to its advantage in predicting short-term prognosis. Conclusion The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient’s clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions. KEY MESSAGES A prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers. The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision. The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied.

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“…Curative radical surgery can be achieved for early-stage patients, but many patients are found to be in the advanced stage and lose the opportunity for radical surgery. In addition, the 5-year survival rate in patients undergoing radical surgery is still poor, less than one-third [3]. The best outcomes depend on the optimal management by specialist multidisciplinary teams [4].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registry

Zhou

Meng

et al. 2022

Preprint

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Background and aims To date, there is still a lack of consensus on the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aims to build a clinical decision support tool based on machine learning of the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Medical Center of PLA General Hospital in China. Methods A total of 4,398 eligible patients with pathology-proven iCCA from the SEER database and 504 from the hospital data were enrolled for modeling by cross-validation based on the method of machine learning. All models were trained by the open-source Python library 4scikit-survival version 0.16.0 and explained by SHapley Additive exPlanations. Permutation importance was calculated using the library ELI5. Results All of the involved treatment modalities can contribute to a better prognosis. Three models were derived and tested among different data sources with the concordance index in the test datasets of 0.67, 0.69, and 0.73 respectively. The prediction was also consistent with the actual situation in randomly selected real patients. Model 2 trained by the hospital data was selected to develop an online tool because of the advantage of predicting the short-term prognosis.Conclusion The prediction model and tool of this study can be applied to predicting patients’ prognosis after treatment by inputting the patient’s clinical parameters or TNM stages and the treatment option and thus contribute to the optimal clinical decision.

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Strategy for improving the prognosis of patients with intrahepatic cholangiocarcinoma by surgical treatment : Considerations based on experience and a literature review

Cited by 3 publications

References 23 publications

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Feasibility of machine learning-based modeling and prediction using multiple centers data to assess intrahepatic cholangiocarcinoma outcomes

Machine learning for clinical decision support in intrahepatic cholangiocarcinoma based on a population study of the US SEER database and a Chinese single-center registry

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