Establishment and Validation of Prognostic Nomograms for Patients With Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 to 2016

Background: Parotid gland adenocarcinoma not otherwise specified (PANOS) is a rare malignant tumor with limited data on its characteristics and prognosis. This research is aimed at characterizing PANOS and developing prognostic prediction models for patients with PANOS.Methods: Cases from 2004–2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) Program database. Univariate and multivariate Cox regression were applied to ascertain the factors associated with survival. Competing risk analysis and Gray's tests were employed to analyze cancer-specific death. Propensity score matching (1:1) was conducted to reduce the influence of confounding variables.Results: A total of 446 patients with a median age of 66 years were selected, of which 307 were diagnosed with stage III/IV PANOS. The 5-year overall survival (OS) rate of all patients was 51.8%, and the median survival time was 66 months. Surgical treatment clearly improved survival time (p < 0.001). In the subgroup analysis, radiotherapy showed survival benefits in patients with stage III/IV disease (p < 0.001). Multivariate Cox regression analyses showed that age, T classification, N classification, M classification and surgery were independent prognostic indicators for OS; T classification, N classification, M classification and surgery were independent risk factors for cancer-specific survival (CSS). In addition, age was independently associated with other cause-specific death. Based on the results of multivariate analysis, two nomograms were developed and verified by the concordance index (C-index) (0.747 and 0.780 for OS and CSS) and the area under the time-dependent receiver operating characteristic (ROC) curve (0.756, 0.764, and 0.819 regarding for nomograms predicting 3-, 5-, and 10- year OS, respectively and 0.794, 0.789, and 0.806 for CSS, respectively).Conclusions: Our study clearly presents the clinicopathological features and survival analysis of patients with PANOS. In addition, our constructed nomogram prediction models may assist physicians in evaluating the individualized prognosis and deciding on treatment for patients.

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Development and Validation of Prognostic Nomograms for Patients with Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 To 2016

10.21203/rs.3.rs-915546/v1 ◽

2021 ◽

Author(s):

Zi-Meng Wang ◽

Zuo-Lin Xiang

Keyword(s):

Parotid Gland ◽

Prediction Models ◽

Cox Regression ◽

Clinicopathological Characteristics ◽

Prognostic Indicators ◽

Regression Analyses ◽

Cancer Specific Survival ◽

T Stage ◽

Not Otherwise Specified ◽

N Stage

Abstract Background Parotid gland adenocarcinoma not otherwise specified (PANOS) is a rare malignancy, and the characteristics and prognosis of this disease remain unclear. This study aims to characterize PANOS and establish prognostic prediction models for patients with PANOS. Methods Cases from 2004–2016 were retrieved from the Surveillance, Epidemiology, and End Results Program database (SEER database). Univariate and multivariate Cox regression analyses, Gray's test and propensity score matching (PSM) were conducted to analyze demographics, treatments, and survival outcomes . Results The 446 patients ( 289 men) selected for analysis had a median age of 66 (19–95) years, and 307 patients were diagnosed with stage III/IV disease. The median survival of all patients was 66 months, with a 51.8% 5-year overall survival (OS) rate. Surgical treatment clearly improved survival time (p < 0.001). In the subgroup analysis, radiotherapy showed survival benefits in patients with advanced-stage disease (III/IV) (p < 0.001). Multivariate Cox regression analyses revealed that age, T stage, N stage, M stage and surgery were independent prognostic indicators for OS;T stage, N stage, M stage and surgery were independent risk factors for cancer-specific survival(CSS).In addition, age was independently associated with noncancer-related death. Two nomograms were established based on the results of the multivariate analysis, which was validated by the concordance index (C-index) (0.747 and 0.780 for OS and CSS, respectively) and the area under the time-dependent receiver operating characteristic(ROC) curve(0.756, 0.764 and 0.819 regarding for nomograms predicting 3-, 5- and 10- year OS, respectively and 0.794, 0.789 and 0.806 for CSS, respectively). Conclusions Our study clearly presents the clinicopathological characteristics and survival analysis of patients with PANOS. In addition, our constructed nomogram prediction models may assist physicians in evaluating the individualized prognosis and deciding on treatment for patients.

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Incidence and Prognostic Factors of Primary Thyroid Lymphoma and Construction of Prognostic Models for Post-chemotherapy and Postoperative Patients: A Population-based Study

10.21203/rs.3.rs-114469/v1 ◽

2020 ◽

Author(s):

Nan Xiang ◽

Fangyuan Dong ◽

Xuebing Zhan ◽

Shuhan Wang ◽

Junjie Wang ◽

...

Keyword(s):

Prognostic Factors ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Models ◽

Steady Increase ◽

Thyroid Lymphoma ◽

Annual Percent Change ◽

Cancer Specific Survival ◽

Good Reliability ◽

Primary Thyroid Lymphoma

Abstract Background: Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. Objective: The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients.Methods: The incidence of PTL in 1975-2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1,616 eligible PTL patients diagnosed in 1998-2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS).Results: PTL incidence showed a relatively steady increase in 1975-1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994-2017(APC -2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. The combination of radiotherapy with chemotherapy or surgery was beneficial to the prognosis of patients. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability.Conclusions: The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

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Non-Hodgkin's lymphomas of childhood: an analysis of the histology, staging, and response to treatment of 338 cases at a single institution.

Journal of Clinical Oncology ◽

10.1200/jco.1989.7.2.186 ◽

1989 ◽

Vol 7 (2) ◽

pp. 186-193 ◽

Cited By ~ 194

Author(s):

S B Murphy ◽

D L Fairclough ◽

R E Hutchison ◽

C W Berard

Keyword(s):

Cox Regression ◽

Specific Treatment ◽

Stage Iv ◽

Stage I ◽

Stage Ii ◽

Stage Iii ◽

Response To Treatment ◽

Clinical Staging ◽

Prognostic Indicators ◽

Hodgkin's Lymphomas

Between 1962 and 1986, a total of 338 consecutive newly diagnosed children and adolescents with non-Hodgkin's lymphomas (NHLs) were evaluated and treated at St Jude Children's Research Hospital (SJCRH). Median follow-up is 6.6 years (range, 1.8 to 23 years). The patients ranged in age from 7 months to 21 years (median, 10 years), and 71% were males. All cases were staged (I to IV) by a clinical staging system. Eighteen percent were stage I, 21% stage II, 43% stage III, and 18% stage IV. Cases frankly leukemic at diagnosis (ie, greater than 25% marrow blasts) were excluded from the analysis. Pathologic material from all cases was reviewed and classified according to the Working Formulation. The histologic distribution of cases was as follows: 38.8% diffuse small non-cleaved cell (undifferentiated, Burkitt's and non-Burkitt's); 26.3% diffuse large-cell, mainly immunoblastic; 28.1% lymphoblastic; and 6.8% other. Treatment policy evolved over time to a stage- and histology-specific strategy for treatment assignment, and overall results significantly improved by era from 37% (+/- 5%) 2-year event-free survival (EFS) for patients treated before 1975 to 77% (+/- 4%) since 1978. By univariate and multivariate Cox regression analyses, the era of treatment (hence, the protocol-specific treatment itself), the stage, and the log of the initial serum lactic dehydrogenase (LDH) emerged as the most powerful prognostic indicators, while histology per se was not significantly related to outcome. For the 154 patients treated since 1978, the 2-year EFS by stage was 97% (+/- 3%) for stage I, 86% (+/- 6%) for stage II, 73% (+/- 6%) for stage III, and 47% (+/- 11%) for stage IV (P less than .0001). Compared with our previous experience, we conclude that the cure rate of childhood NHL has doubled in the last decade with modern management.

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Incidence and prognostic factors of primary thyroid lymphoma and construction of prognostic models for post-chemotherapy and postoperative patients: a population-based study

BMC Endocrine Disorders ◽

10.1186/s12902-021-00732-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Nan Xiang ◽

Fangyuan Dong ◽

Xuebing Zhan ◽

Shuhan Wang ◽

Junjie Wang ◽

...

Keyword(s):

Prognostic Factors ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Models ◽

Steady Increase ◽

Thyroid Lymphoma ◽

Annual Percent Change ◽

Cancer Specific Survival ◽

Good Reliability ◽

Primary Thyroid Lymphoma

Abstract Background Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients. Methods The incidence of PTL in 1975–2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1616 eligible PTL patients diagnosed in 1998–2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS). Results PTL incidence showed a relatively steady increase in 1975–1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994–2017(APC − 2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability. Conclusions The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

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Metabolic syndrome and upper tract urothelial carcinoma: A retrospective analysis from a large Chinese cohort.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.452 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 452-452

Author(s):

Hang Xu ◽

Ping Tan ◽

Lu Yang ◽

Qiang Wei

Keyword(s):

Metabolic Syndrome ◽

Urothelial Carcinoma ◽

Prediction Models ◽

Cox Regression ◽

Density Lipoprotein ◽

Upper Tract Urothelial Carcinoma ◽

Survival Outcomes ◽

Cancer Specific Survival ◽

Cox Regression Analysis ◽

Upper Tract

452 Background: Metabolic syndrome (MetS) has been reported to be associated with poor survival outcomes in cancer patients. However, the role of MetS in upper tract urothelial carcinoma (UTUC) has yet to be explored. We aim to investigate the prognostic value of MetS in UTUC after radical nephroureterectomy (RNU). Methods: A total of 644 patients with UTUC after RNU were identified at West China Hospital from May 2003 to December 2016. MetS was defined as the co-existence of three or more of five components (obesity, hypertension, elevated fasting glucose, decreased high-density lipoprotein-cholesterol and hypertriglyceridemia). Logistic and Cox regression analyses was performed to evaluate the associations of MetS with pathological features and survival outcomes. Decision curve analysis was performed to determine the clinical utility of the prediction models. Results: Of 644 patients, 157 (24.4%) had MetS. Over a median follow-up of 39 months, 269 (41.8%) experienced disease recurrence, 233 (36.2%) died and 185 (28.7%) died of UTUC. MetS was independently associated with high-grade disease (odds ratio [OR]: 2.01, P = 0.005), advanced pT stage (≥ pT3, OR: 1.54, P = 0.027) and lymphovascular invasion (OR: 1.71, P = 0.03). Multivariate Cox regression analysis showed that MetS was an independent factor for decreased cancer-specific survival (CSS, HR: 1.38, 95% CI: 1.01-1.89, P = 0.042) but not for RFS (HR: 1.27, 95% CI: 0.97-1.67, P = 0.078) and OS (HR: 1.24, 95% CI: 0.95-1.62, P = 0.121). The estimated c-index of the multivariate models for CSS was 0.763 compared with 0.769 when MetS added. Conclusions: MetS is a negative prognostic factor in UTUC. Further studies of MetS in UTUC are demanded.

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Combination of tissues analysis and immune infiltrate in localized colon cancer using artificial intelligence in PETACC8 study.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.3574 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 3574-3574

Author(s):

Cynthia Reichling ◽

Julien Taieb ◽

Valentin Derangere ◽

Karine Le Malicot ◽

Jean Francois Emile ◽

...

Keyword(s):

Artificial Intelligence ◽

Colon Cancer ◽

Outcome Prediction ◽

Prediction Models ◽

Cox Regression ◽

Disease Free Survival ◽

Stage Iii ◽

Outcome Variable ◽

Stage Iii Colon Cancer ◽

Automated Method

3574 Background: We used artificial intelligence to perform tissue classification and count CD3 and CD8 in each subclass and determined their role in outcome prediction in PETACC8 cohort of stage III colon cancer treated with FOLFOX or FOLFOX plus cetuximab. Methods: We developed artificial intelligence aimed to detect tumor, healthy mucosa, stroma and immune cells on whole slide of CD3 and CD8 staining. The invasive margin (IM) was also automatically determined. Using a lasso algorithm, the software was able to detect digital parameters within the tumor core (TC) which were related to patients’ outcome (variable called DGMate for DiGital tuMor pArameTErs). CD3 and CD8 lymphocytes density were also quantified automatically by the software in TC and at IM. Associations with disease-free survival (DFS) were evaluated by multivariable Cox regression adjusting for age, T/N stage, sidedness, KRAS/BRAF, DNA mismatch repair (MMR). Results: On 1220 samples collected, data could be generated for 1018 patients. We observed that a high IM stromal area and a high DGMate were associated with a poorer DFS [HR 5.65 (95% CI, 2.34, 13.67), p < 0.0001; HR 2.72 (95% IC, 1.92, 3.85), p<0.001 respectively for the continuous variable]. A higher density of CD3+ TC, CD3+ IM and CD8+ TC were significantly associated with a longer DFS (HR 0.75 (95% IC, .66, .87), p<0.0001; HR 0.78 (95% IC, .68, .88), p<0.0001; HR 0.83 (95% IC, .71, .96), p=0.01). All these immune variables were significantly correlated with each other. ANOVA test demonstrated that CD3+ TC gave a similar prognostic value compared to the classical CD3/CD8 immunoscore (p=0.44). The combination of IM stromal area, DGMate and CD3 outperformed the classical CD3/CD8 immunoscore to estimate patients’ prognosis (C-index= 0.601 vs 0.578, p-value=0.04). Adding this new variable to classical clinical prognostic parameters we generated a nomogram which predicted the risk of relapse of stage III colon cancer with a stronger predictive value compared to clinical parameters or the immunoscore. Conclusions: We propose a new fully automated method of whole slide analysis using a software based on artificial intelligence which classify tissue and determine tumor and immune parameters on one single slide stained with CD3 antibody. This valuable strategy outperforms immunoscore and clinical outcome prediction models.

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Decreased LKB1 predicts poor prognosis in Pancreatic Ductal Adenocarcinoma

Scientific Reports ◽

10.1038/srep10575 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 12

Author(s):

Jian-Yu Yang ◽

Shu-Heng Jiang ◽

De-Jun Liu ◽

Xiao-Mei Yang ◽

Yan-Miao Huo ◽

...

Keyword(s):

Protein Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Cox Regression ◽

Clinical Stage ◽

Ductal Adenocarcinoma ◽

Kaplan Meier ◽

Tumor Tissues ◽

N Classification ◽

Liver Kinase B1 ◽

T Classification

Abstract Liver kinase B1 (LKB1) has been identified as a critical modulator involved in cell proliferation and polarity. The purpose of the current study was to characterize the expression pattern of LKB1 and assess the clinical significance of LKB1 expression in pancreatic ductal adenocarcinoma (PDAC) patients. LKB1 mRNA expression which was analyzed in 32 PDAC lesions and matched non-tumor tissues, was downregulated in 50% (16/32) of PDAC lesions. Similar results were also obtained by analyzing three independent datasets from Oncomine. Protein expression of LKB1 was significantly reduced in 6 PDAC cell lines and downregulated in 31.3% (10/32) of PDAC lesions compared to matched non-tumorous tissues, as determined by Western blot analysis. Additionally, tissue microarray containing 205 PDAC specimens was evaluated for LKB1 expression by IHC and demonstrated that reduced expression of LKB1 in 17.6% (36/205) of PDAC tissues was significantly correlated with clinical stage, T classification, N classification, liver metastasis and vascular invasion. Importantly, Kaplan-Meier survival and Cox regression analyses were executed to evaluate the prognosis of PDAC and found that LKB1 protein expression was one of the independent prognostic factors for overall survival of PDAC patients.

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A Risk Stratification Model Based on a Population Analysis for Predicting Cancer Specific Survival in Pediatric Brain Stem Glioma.

10.22541/au.162791899.95558477/v1 ◽

2021 ◽

Author(s):

Kai Sun ◽

Xiaowei Fei ◽

Mingwei Xu ◽

Wenjin Chen ◽

Ruxiang Xu

Keyword(s):

Multivariate Analysis ◽

Risk Stratification ◽

Cox Regression ◽

Population Analysis ◽

Brainstem Glioma ◽

Prognostic Indicators ◽

Brain Stem Glioma ◽

Clinicopathologic Characteristics ◽

Cancer Specific Survival ◽

Risk Stratification Model

Introduction: The aim of this study was to construct and validate a nomogram and risk stratification model for predicting cancer-specific survival (CSS) of pediatric brainstem glioma patients. Methods: Cases of pediatric brainstem glioma patients (<12 years) from 1998 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and demographic, clinicopathologic characteristics, treatments, and survival outcomes were analyzed. The total cohort was randomly divided into training and validation sets, followed by univariate and multivariate Cox regression analyses. A nomogram was constructed and risk stratification analysis incorporated using the selected variables from the multivariate analysis. The accuracy of the model was assessed using C-index and calibration curves. Results: A total of 806 pediatric cases with histologically confirmed diagnosis of brainstem glioma were selected and analyzed. Multivariate analysis showed that age, race, tumor size, grade and radiotherapy (P<0.05) were independent prognostic indicators of pediatric gliomas. For prediction of CSS, the C-index of the nomogram was 0.75, which shows a good predictive probability. Conclusion: The nomogram developed in this study for predicting survival of pediatric patients with histologically confirmed stem gliomas is the first to incorporate risk stratification. Combining nomogram and risk stratification system is a convenient tool to aid clinicians in the identification of high-risk patients and to perform targeted adjuvant treatment.

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Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA–miRNA–mRNA networks and Cox regression models

Bioscience Reports ◽

10.1042/bsr20191554 ◽

2020 ◽

Vol 40 (1) ◽

Cited By ~ 3

Author(s):

Yan Yao ◽

Tingting Zhang ◽

Lingyu Qi ◽

Ruijuan Liu ◽

Gongxi Liu ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Prediction Models ◽

Cox Regression ◽

Prognostic Index ◽

The Cancer Genome Atlas ◽

Prognostic Biomarkers ◽

Prognostic Indicators ◽

Cox Regression Analysis ◽

Cerna Network ◽

A Genome

Abstract Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide, and its underlying mechanism remains unclear. Accumulating evidence has highlighted that long non-coding RNA (lncRNA) acts as competitive endogenous RNA (ceRNA) and plays an important role in the occurrence and development of LUAD. Here, we comprehensively analyzed and provided an overview of the lncRNAs, miRNAs, and mRNAs associated with LUAD from The Cancer Genome Atlas (TCGA) database. Then, differentially expressed lncRNAs (DElncRNA), miRNAs (DEmiRNA), and mRNAs (DEmRNA) were used to construct a lncRNA–miRNA–mRNA regulatory network according to interaction information from miRcode, TargetScan, miRTarBase, and miRDB. Finally, the RNAs of the network were analyzed for survival and submitted for Cox regression analysis to construct prognostic indicators. A total of 1123 DElncRNAs, 95 DEmiRNAs, and 2296 DEmRNAs were identified (|log2FoldChange| (FC) > 2 and false discovery rate (FDR) or adjusted P value < 0.01). The ceRNA network was established based on this and included 102 lncRNAs, 19 miRNAs, and 33 mRNAs. The DEmRNAs in the ceRNA network were found to be enriched in various cancer-related biological processes and pathways. We detected 22 lncRNAs, 12 mRNAs, and 1 miRNA in the ceRNA network that were significantly associated with the overall survival of patients with LUAD (P < 0.05). We established three prognostic prediction models and calculated the area under the 1,3,5-year curve (AUC) values of lncRNA, mRNA, and miRNA, respectively. Among them, the prognostic index (PI) of lncRNA showed good predictive ability which was 0.737, 0.702 and 0.671 respectively, and eight lncRNAs can be used as candidate prognostic biomarkers for LUAD. In conclusion, our study provides a new perspective on the prognosis and diagnosis of LUAD on a genome-wide basis, and develops independent prognostic biomarkers for LUAD.

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Survival after surgery among patients with cholangiocarcinoma in Northeast Thailand according to anatomical and morphological classification

BMC Cancer ◽

10.1186/s12885-021-08247-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chaiwat Tawarungruang ◽

Narong Khuntikeo ◽

Nittaya Chamadol ◽

Vallop Laopaiboon ◽

Jaruwan Thuanman ◽

...

Keyword(s):

Survival Rate ◽

Survival Time ◽

Median Survival ◽

Median Survival Time ◽

Cox Regression ◽

Survival Rates ◽

Care Program ◽

Tumor Location ◽

Northeast Thailand ◽

Curative Surgery

Abstract Background Cholangiocarcinoma (CCA) has been categorized based on tumor location as intrahepatic (ICCA), perihilar (PCCA) or distal (DCCA), and based on the morphology of the tumor of the bile duct as mass forming (MF), periductal infiltrating (PI) or intraductal (ID). To date, there is limited evidence available regarding the survival of CCA among these different anatomical and morphological classifications. This study aimed to evaluate the survival rate and median survival time after curative surgery among CCA patients according to their anatomical and morphological classifications, and to determine the association between these classifications and survival. Methods This study included CCA patients who underwent curative surgery from the Cholangiocarcinoma Screening and Care Program (CASCAP), Northeast Thailand. The anatomical and morphological classifications were based on pathological findings after surgery. Survival rates of CCA and median survival time since the date of CCA surgery and 95% confidence intervals (CI) were calculated. Multiple cox regression was performed to evaluate factors associated with survival which were quantified by hazard ratios (HR) and their 95% CIs. Results Of the 746 CCA patients, 514 had died at the completion of the study which constituted 15,643.6 person-months of data recordings. The incidence rate was 3.3 per 100 patients per month (95% CI: 3.0–3.6), with median survival time of 17.8 months (95% CI: 15.4–20.2), and 5-year survival rate of 24.6% (95% CI: 20.7–28.6). The longest median survival time was 21.8 months (95% CI: 16.3–27.3) while the highest 5-year survival rate of 34.8% (95% CI: 23.8–46.0) occurred in the DCCA group. A combination of anatomical and morphological classifications, PCCA+ID, was associated with the longest median survival time of 40.5 months (95% CI: 17.9–63.0) and the highest 5-year survival rate of 42.6% (95% CI: 25.4–58.9). The ICCA+MF combination was associated with survival (adjusted HR: 1.45; 95% CI: 1.01–2.09; P = 0.013) compared to ICCA+ID patients. Conclusions Among patients receiving surgical treatment, those with PCCA+ID had the highest 5-year survival rate, which was higher than in groups classified by only anatomical characteristics. Additionally, the patients with ICCA+MF tended to have unfavorable surgical outcomes. Showed the highest survival association. Therefore, further investigations into CCA imaging should focus on patients with a combination of anatomical and morphological classifications.

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