Dataset Information

Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

ABSTRACT: Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumor location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymph nodes, number of positive lymph nodes, tumor size, and metastatic surgery as independent predictors for CRCLM. A nomogram incorporating the 10 predictors was constructed. The nomogram showed favorable sensitivity at predicting 1-, 3-, and 5-year OS, with area under the receiver operating characteristic curve (AUROC) values of 0.816, 0.782, and 0.787 in the training cohort; 0.827, 0.769, and 0.774 in the internal validation cohort; and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort; 0.828, 0.753, and 0.758 in the internal validation cohort; and 0.828, 0.737, and 0.772 in the external validation cohort, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single methods. In summary, the novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.

SUBMITTER: Cao Y

PROVIDER: S-EPMC8671306 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

Cao Yinghao Y Ke Songqing S Deng Shenghe S Yan Lizhao L Gu Junnan J Mao Fuwei F Xue Yifan Y Zheng Changmin C Cai Wentai W Liu Hongli H Li Han H Shang Fumei F Sun Zhuolun Z Wu Ke K Zhao Ning N Cai Kailin K

Frontiers in oncology 20211201

Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent ...[more]

PMID: 34926243

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Project description:BackgroundPatients experiencing severe postoperative pain often show lower adherence to prescribed treatments, highlighting the clinical need for effective pain prediction and management strategies. This study aims to address this gap by identifying key risk factors associated with post-transarterial chemoembolization (TACE) pain and developing a predictive scoring system.MethodsWe retrospectively analyzed data from liver cancer patients who underwent their first TACE procedure at our institution between January 2019 and December 2020. Pain levels were assessed using an 11-point numerical rating scale (NRS-11). Patients were randomly assigned to training and validation cohorts. In the training cohort, logistic regression was used to evaluate the correlation between various parameters and post-TACE pain, leading to the development of a risk prediction model. This model's performance was subsequently assessed in the validation cohort.ResultsThe study included 255 patients. Univariate analysis in the training cohort identified tumor number, size, microsphere volume, and operation time as factors associated with postoperative pain. These factors were included in a multivariate model, which demonstrated areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.71 in the training cohort and 0.74 in the validation cohort for predicting moderate to severe pain. A nomogram was also developed for clinical application, categorizing patients with scores above 72.90 as high risk for moderate to severe pain.ConclusionsOur research successfully developed and validated a novel scoring system capable of predicting moderate to severe pain following initial TACE treatment. However, the study's predictive accuracy, as reflected by AUC values, suggests that further refinement and validation in larger, diverse cohorts are necessary to enhance its clinical utility. This work underscores the importance of predictive tools in improving postoperative pain management and patient outcomes.

Project description:ObjectivesTo develop and validate a diagnostic scoring system to differentiate intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM).MethodsA total of 366 patients (263 in the training cohort, 103 in the validation cohort) who underwent MRI examination with pathologically proven either IMCC or CRLM from two centers were included. Twenty-eight MRI features were collected. Univariate analyses and multivariate logistic regression analyses were performed to identify independent predictors for distinguishing IMCC from solitary CRLM. The independent predictors were weighted over based on regression coefficients to build a scoring system. The overall score distribution was divided into three groups to show the diagnostic probability of CRLM.ResultsSix independent predictors, including hepatic capsular retraction, peripheral hepatic enhancement, vessel penetrating the tumor, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, and rim enhancement at the portal venous phase were included in the system. All predictors were assigned 1 point. At a cutoff of 3 points, AUCs for this score model were 0.948 and 0.903 with sensitivities of 96.5% and 92.0%, specificities of 84.4% and 71.7%, positive predictive values of 87.7% and 75.4%, negative predictive values of 95.4% and 90.5%, and accuracies of 90.9% and 81.6% for the training and validation cohorts, respectively. An increasing trend was shown in the diagnostic probability of CRLM among the three groups based on the score.ConclusionsThe established scoring system is reliable and convenient for distinguishing IMCC from solitary CRLM using six MRI features.Clinical relevance statementA reliable and convenient scoring system was developed to differentiate between intrahepatic mass-forming cholangiocarcinoma from solitary colorectal liver metastasis using six MRI features.Key points• Characteristic MRI features were identified to distinguish intrahepatic mass-forming cholangiocarcinoma (IMCC) from solitary colorectal liver metastasis (CRLM). • A model to distinguish IMCC from solitary CRLM was created based on 6 features, including hepatic capsular retraction, upper abdominal lymphadenopathy, peripheral washout at the portal venous phase, rim enhancement at the portal venous phase, peripheral hepatic enhancement, and vessel penetrating the tumor.

Project description:BackgroundThe survival benefit of primary and metastatic resection for patients with colorectal cancer with liver metastasis (CRLM) has been observed, but methods for discriminating which individuals would benefit from surgery have been poorly defined. Herein, a predictive model was developed to stratify patients into sub-population based on their response to surgery.MethodsWe assessed the survival benefits for adults diagnosed with colorectal liver metastasis by comparing patients with curative surgery vs. those without surgery. CRLM patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction. Other data including CRLM patients from our center were obtained for external validation. Calibration plots, the area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with the tumor-node-metastasis (TNM) classification. The Kaplan-Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery.ResultsA total of 1,220 eligible patients were identified, and 881 (72.2%) underwent colorectal and liver resection. Cancer-specific survival (CSS) for the surgery group was significantly better than that for the no-surgery group (41 vs. 14 months, p < 0.001). Five factors were found associated with CSS and adopted to build the nomograms, i.e., age, T stage, N stage, neoadjuvant chemotherapy, and primary tumor position. The AUC of the CRLM nomogram showed a better performance in identifying patients who could obtain benefits in the surgical treatment, compared with TNM classification (training set, 0.826 [95% CI, 0.786-0.866] vs. 0.649 [95% CI, 0.598-0.701]; internal validation set, 0.820 [95% CI, 0.741-0.899] vs. 0.635 [95% CI, 0.539-0.731]; external validation set, 0.763 [95% CI, 0.691-0.836] vs. 0.626 [95% CI, 0.542-0.710]). The calibration curves revealed excellent agreement between the predicted and actual survival outcomes. The DCA showed that the nomogram exhibited more clinical benefits than the TNM staging system. The beneficial and surgery group survived longer significantly than the non-beneficial and surgery group (HR = 0.21, 95% CI, 0.17-0.27, p < 0.001), but no difference was observed between the non-beneficial and surgery and non-surgery groups (HR = 0.89, 95% CI, 0.71-1.13, p = 0.344).ConclusionsAn accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for the resection of primary and metastatic lesions among CRLM patients.

Project description:BackgroundSimultaneous resections have been increasingly performed for colorectal liver metastasis patients. However, studies explored risk stratification for these patients are scarce. Among which, a clear definition of early recurrence remains controversial and models for predicting early recurrence in these patients are lacking.MethodsColorectal liver metastasis patients who developed recurrence followed by simultaneous resection were enrolled. Early recurrence was determined by the minimum P value method, and patients were divided into an early recurrence group and late recurrence group. Standard clinical data were collected from each patient including demographics features, preoperative laboratory tests and postoperative regular follow-up results. All the data were accessed by clinicians and recorded accordingly. The nomogram for early recurrence was constructed in the training cohort and validated externally in the test cohort.ResultsThe optimal value of early recurrence by the minimum P value method was 13 months. A total of 323 patients were included in the training cohort, of which 241 (74.6%) experienced early recurrence. Seventy-one patients were included in the test cohort, of which 49 (69.0%) experienced early recurrence. Significantly worse post-recurrence survival (median 27.0 vs. 52.8 months, P=0.00083) and overall survival (median 33.8 vs. 70.9 months, P<0.0001) were observed in patients with early recurrence in the training cohort. Positive lymph node metastases (P=0.003), tumour burden scores ≥4.09 (P=0.001), preoperative neutrophil-to-lymphocyte ratios ≥1.44 (P=0.006), preoperative blood urea nitrogen levels ≥3.55 µmol/L (P=0.017) and postoperative complications (P=0.042) were independently associated with early recurrence, and all these predictors were included in the nomogram. The nomogram for predicting early recurrence had a receiver operating characteristic curve of 0.720 in the training cohort and a receiver operating characteristic curve of 0.740 in the test cohort. The Hosmer-Lemeshow test and calibration curves showed acceptable model calibration in the training set (P=0.7612) and in the test set (P=0.8671). The decision curve analysis results for the training cohort and test cohort also indicated that the nomogram showed good clinical applicability.ConclusionsOur findings provide clinicians with new insights into accurate risk stratification for colorectal liver metastasis patients receiving simultaneous resection and contributing to the management of patients.

Dataset Information

Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

Publications

Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study.

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