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

2021 ◽  
Vol 11 ◽  
Author(s):  
Yinghao Cao ◽  
Songqing Ke ◽  
Shenghe Deng ◽  
Lizhao Yan ◽  
Junnan Gu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Tumor Location ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Training Cohort

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.

scholarly journals A Modified TNM Classification for Primary Operable Colorectal Cancer

2020 ◽  
Author(s):  
Chundong Zhang ◽  
Zubing Mei ◽  
Junpeng Pei ◽  
Masanobu Abe ◽  
Xiantao Zeng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Validation Cohort ◽  
Tnm Classification ◽  
Characteristic Curve ◽  
External Validation ◽  
Model Fitting ◽  
Information Criteria ◽  
Stage I ◽  
Internal Validation ◽  
Clinical Benefits

Abstract Background The American Joint Committee on Cancer (AJCC) 8th tumor/node/metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods We included 45,379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (N =31,772) or an internal validation cohort (N =13,607). External validation was performed in 10,902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve (AUC) and Akaike information criteria (AIC) were applied for prognostic discrimination and model-fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a), IIA (T1N1b, T2N1b, T3N0), IIB (T1-2N2a-2b, T3N1a-1b, T4aN0), IIC (T3N2a, T4aN1a-2a, T4bN0), IIIA (T3N2b, T4bN1a), IIIB (T4aN2b, T4bN1b), and IIIC (T4bN2a-2b). In the internal validation cohort, compared to the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (AUC = 0.675 vs. 0.667, respectively; two-sided P <0.001) and better model-fitting (AIC = 70,937 vs. 71,238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.

scholarly journals Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

2019 ◽  
Vol 50 (3) ◽  
pp. 261-269
Author(s):  
Jieyun Zhang ◽  
Yue Yang ◽  
Xiaojian Fu ◽  
Weijian Guo
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Prognosis ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

P0616REAL-TIME CLINICAL DECISION SUPPORT TO PREDICT AND MANAGE IN-HOSPITAL ACUTE KIDNEY INJURY UTILIZING RECURRENT NEURAL NETWORK

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hea Eun Kim ◽  
Hyeonsik Yang ◽  
Sejoong Kim ◽  
Kipyo Kim
Keyword(s):  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Vital Signs ◽  
Clinical Decision ◽  
Internal Validation ◽  
Training Cohort ◽  
National University ◽  
Seoul National University ◽  
Dynamic Variables

Abstract Background and Aims Rapidly Increasing electronic health record (EHR) data and recent development of machine learning methods offers the possibilities of improvement in quality of care in clinical practice. Machine learning can incorporate huge amount of features into the model, and enable non-linear algorithms with great performance. Previously published AKI prediction models have simple design without real-time assessment. Major risk factors in in-hospital AKI include use of various nephrotoxins, repeatedly measured laboratory findings, and vital signs, which are dynamic variables rather than static. Given that recurrent neural network (RNN) is a powerful tool to handle the sequential data, using RNN method in the prediction model is a promising approach. Therefore, in the present study, we proposed a RNN-based prediction model with external validation for in-hospital AKI and aimed to provide a framework to link the developed model with clinical decision supports. Method Study populations were all patients aged ≥ 18 years and hospitalized more than a week at Seoul National University Bundang Hospital (SNUBH) from 2013 to 2017 (training cohort) and at Seoul National University Hospital (SNUH) in 2017 (validation cohort). All demographics, laboratory values, vital signs, and clinical conditions were obtained from the EHR of each hospital. A total of 102 variables included in the model. Each variable falls into two categories: static and dynamic variable; static variable was time-invariant values during hospitalization, and dynamic variables were daily-updated values. Baseline creatinine was determined by searching the minimum serum Cr level within 2 weeks before admission. We developed two different models (model 1 and model 2) using RNN algorithms. The outcome for model 1 was the occurrence of AKI within 7 days from the present. In model 2, we constructed the prediction model of the trajectory of Cr values after 24 hours, 48 hours, and 72 hours, using available Cr values from 7 days ago to the present. Internal validation was performed by 5-fold cross validation using the training set (SNUBH), and then external validation was done using test set (SNUH). Results A total of 40,552 patients in training cohort and 4,000 patients in external validation cohort (test cohort) were included in the study. The mean age of participants was 62.2 years in training cohort and 58.7 years in test cohort. Baseline eGFR was 93.8 ± 40.4 ml/min/1.73m2 in training cohort and 88.4 ± 23.2 ml/min/1.73m2 in test cohort. In model 1 for the prediction of AKI occurrence within 7 days, the area under the curve was 0.93 (sensitivity 0.90, specificity 0.96) in internal validation, and 0.83 (sensitivity 0.83, specificity 0.82) in external validation. The model 2 predicted the creatinine trajectory within 3 days accurately; root mean square error was 0.1 in training cohort and 0.3 in test cohort. To support the clinical decision for AKI manage, we estimated the predicted trajectories of future creatinine levels after renal insult removal, such as nephrotoxic drugs, based on the established model 2. Conclusion We developed and validated a real-time AKI prediction model using RNN algorithms. This model showed high performance and can accurately visualize future creatinine trajectories. In addition, the model can provide the information about modifiable factors in patients with high risk of AKI.

scholarly journals Construction and External Validation of a Nomogram for Predicting Survival of Intrahepatic Cholangiocarcinoma Patients Without Lymph Node and Distant Metastasis

2021 ◽  
Author(s):  
Chuang Jiang ◽  
Fei Teng ◽  
Yunyou Tang ◽  
Ziqi Zhang ◽  
Yimin Chen ◽  
...  
Keyword(s):  
Lymph Node ◽  
Intrahepatic Cholangiocarcinoma ◽  
Validation Cohort ◽  
External Validation ◽  
Staging System ◽  
Tnm Staging ◽  
Internal Validation ◽  
West China Hospital ◽  
Training Cohort ◽  
West China

Abstract BackgroundThe purpose of this study was to construct and external validate a nomogram for predicting overall survival(OS) in intrahepatic cholangiocarcinoma (ICC) patients classified as N0M0 according to the 7th edition of American Joint Committee on Cancer (AJCC) TNM staging system.Methods:812 ICC patients without distant and lymph node metastasis between 2011 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database, then randomly assigned to the training cohort(n=648) or internal validation cohort(n=164), external validation cohort consisted of 136 ICC patients with N0M0 stage treated in West China Hospital of Sichuan University from 2013 to 2015. The precision of the nomogram was validated internally using SEER validation cohort and externally using the patients’ data of West China Hospital. Results :The nomogram was established to predict 1-year, 3-year and 5-year OS and the calibration curve showed nomogram prediction performance was in good agreement with the actual results. The C‑index of the nomogram was 0.750(95% CI:0.731-0.769) in the training cohort, and the internal and external validated C-indexes were 0.803(95% CI:0.783-0.823) and 0.681(95% CI:0.524-0.838), respectively. In the training, internal and external validation cohort, the 1-year, 3‑year and 5‑year AUCs were (0.772,0.809,0.798),(0.896,0.868,0.896) and (0.673,0.786,0.886), respectively.Conclusions This nomogram has an excellent predictive effect on the 1- ,3-, 5-year OS of ICC patients with stage N0M0 and guide the optimal treatment for these type of patients.

Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 531-539 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Texture Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Hepatic Metastases ◽  
Survival Prediction ◽  
Training Cohort ◽  
Link Type ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria

PurposeThe objective of this study was to build and validate a radiomic signature to predict early a poor outcome using baseline and 2-month evaluation CT and to compare it to the RECIST1·1 and morphological criteria defined by changes in homogeneity and borders.MethodsThis study is an ancillary study from the PRODIGE-9 multicentre prospective study for which 491 patients with metastatic colorectal cancer (mCRC) treated by 5-fluorouracil, leucovorin and irinotecan (FOLFIRI) and bevacizumab had been analysed. In 230 patients, computed texture analysis was performed on the dominant liver lesion (DLL) at baseline and 2 months after chemotherapy. RECIST1·1 evaluation was performed at 6 months. A radiomic signature (Survival PrEdiction in patients treated by FOLFIRI and bevacizumab for mCRC using contrast-enhanced CT TextuRe Analysis (SPECTRA) Score) combining the significant predictive features was built using multivariable Cox analysis in 120 patients, then locked, and validated in 110 patients. Overall survival (OS) was estimated with the Kaplan-Meier method and compared between groups with the logrank test. An external validation was performed in another cohort of 40 patients from the PRODIGE 20 Trial.ResultsIn the training cohort, the significant predictive features for OS were: decrease in sum of the target liver lesions (STL), (adjusted hasard-ratio(aHR)=13·7, p=1·93×10–7), decrease in kurtosis (ssf=4) (aHR=1·08, p=0·001) and high baseline density of DLL, (aHR=0·98, p<0·001). Patients with a SPECTRA Score >0·02 had a lower OS in the training cohort (p<0·0001), in the validation cohort (p<0·0008) and in the external validation cohort (p=0·0027). SPECTRA Score at 2 months had the same prognostic value as RECIST at 6 months, while non-response according to RECIST1·1 at 2 months was not associated with a lower OS in the validation cohort (p=0·238). Morphological response was not associated with OS (p=0·41).ConclusionA radiomic signature (combining decrease in STL, density and computed texture analysis of the DLL) at baseline and 2-month CT was able to predict OS, and identify good responders better than RECIST1.1 criteria in patients with mCRC treated by FOLFIRI and bevacizumab as a first-line treatment. This tool should now be validated by further prospective studies.Trial registrationClinicaltrial.gov identifier of the PRODIGE 9 study: NCT00952029.Clinicaltrial.gov identifier of the PRODIGE 20 study: NCT01900717.

scholarly journals A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

2022 ◽  
Vol 9 ◽  
Author(s):  
Taiyu He ◽  
Tianyao Chen ◽  
Xiaozhu Liu ◽  
Biqiong Zhang ◽  
Song Yue ◽  
...  
Keyword(s):  
Elderly Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Risk Groups ◽  
Cancer Specific Survival ◽  
Web Based ◽  
Training Cohort ◽  
Kaplan Meier ◽  
Early Hcc ◽  
High Consistency

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 540-550 ◽  
Author(s):  
Shulin Yu ◽  
Yuchen Li ◽  
Zhuan Liao ◽  
Zheng Wang ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Extracellular Vesicle ◽  
Carbohydrate Antigen ◽  
Superior Performance ◽  
Ductal Adenocarcinoma ◽  
Support Vector ◽  
Internal Validation

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

scholarly journals Radiomics Based on Contrast-Enhanced MRI in Differentiation Between Fat-Poor Angiomyolipoma and Hepatocellular Carcinoma in Noncirrhotic Liver: A Multicenter Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangtian Zhao ◽  
Yukun Zhou ◽  
Yuan Zhang ◽  
Lujun Han ◽  
Li Mao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Arterial Phase ◽  
Combined Model ◽  
Internal Validation ◽  
Contrast Enhanced

ObjectiveThis study aims to develop and externally validate a contrast-enhanced magnetic resonance imaging (CE-MRI) radiomics-based model for preoperative differentiation between fat-poor angiomyolipoma (fp-AML) and hepatocellular carcinoma (HCC) in patients with noncirrhotic livers and to compare the diagnostic performance with that of two radiologists.MethodsThis retrospective study was performed with 165 patients with noncirrhotic livers from three medical centers. The dataset was divided into a training cohort (n = 99), a time-independent internal validation cohort (n = 24) from one center, and an external validation cohort (n = 42) from the remaining two centers. The volumes of interest were contoured on the arterial phase (AP) images and then registered to the venous phase (VP) and delayed phase (DP), and a total of 3,396 radiomics features were extracted from the three phases. After the joint mutual information maximization feature selection procedure, four radiomics logistic regression classifiers, including the AP model, VP model, DP model, and combined model, were built. The area under the receiver operating characteristic curve (AUC), diagnostic accuracy, sensitivity, and specificity of each radiomics model and those of two radiologists were evaluated and compared.ResultsThe AUCs of the combined model reached 0.789 (95%CI, 0.579–0.999) in the internal validation cohort and 0.730 (95%CI, 0.563–0.896) in the external validation cohort, higher than the AP model (AUCs, 0.711 and 0.638) and significantly higher than the VP model (AUCs, 0.594 and 0.610) and the DP model (AUCs, 0.547 and 0.538). The diagnostic accuracy, sensitivity, and specificity of the combined model were 0.708, 0.625, and 0.750 in the internal validation cohort and 0.619, 0.786, and 0.536 in the external validation cohort, respectively. The AUCs for the two radiologists were 0.656 and 0.594 in the internal validation cohort and 0.643 and 0.500 in the external validation cohort. The AUCs of the combined model surpassed those of the two radiologists and were significantly higher than that of the junior one in both validation cohorts.ConclusionsThe proposed radiomics model based on triple-phase CE-MRI images was proven to be useful for differentiating between fp-AML and HCC and yielded comparable or better performance than two radiologists in different centers, with different scanners and different scanning parameters.

scholarly journals A Novel Clinical Nomogram for Predicting Lymph Node Metastasis in Ovarian Cancer: A SEER Analysis and External Validation in a Tertiary Center

2021 ◽  
Author(s):  
Guiping Zhang ◽  
Wei Ren
Keyword(s):  
Logistic Regression ◽  
Lymph Node ◽  
Validation Cohort ◽  
External Validation ◽  
Internal Validation ◽  
Training Cohort ◽  
Preoperative Serum ◽  
Histology Grade ◽  
Serum Ca125 ◽  
Serum Ca125 Level

Abstract Introduction The aim of the study is to investigate the risk factors for developing lymph node metastases (LNM) in cases diagnosed as a presumed early-stage ovarian carcinoma (OC). Methodology Information of patients who had been diagnosed as OC in 2018 was obtained from the SEER database. We enrolled 104 OC patients in General Hospital of Northern Theatre Command for external validation. A logistic regression was conducted to determine the independent predictors for LNM, which were used for establishing a nomogram. In order to evaluate the reliability of nomogram, we applied a receiver operating characteristic curve (ROC) analysis, calibration curves and plotted decision curves. Results We found that age(≥70, OR=0.544, p=0.022), histology type (Mucinous carcinoma, OR=0.390, p=0.001; Endometrioid carcinoma, OR=7.946, p=0.053; Others, OR=2.400, p=0.040), histology grade (Grade II, OR=2.423, p=0.028; Grade III, OR=1.982, p=0.152; Grade IV, OR=1.594, p=0.063) and preoperative serum CA125 level (positive, OR=2.236, p=0.001) were all significant predictors of LNM. The AUC of the model training cohort, internal validation cohort, and external validation cohort were 0.78, 0.79 and 0.76 respectively. The calibration curves showed that the predicted outcome fitted well to the observed outcome in the training cohort (p=0.825) internal validation cohort (p=0.503), and external validation cohort (p=0.108). The decision curves showed the nomogram had more benefits than the All or None scheme if the threshold probability is >50% and <100% in training cohort and internal validation cohort, >30% and <90% in the external validation cohort. Conclusion The multivariate logistic regression showed that age, histology type, histology grade and preoperative serum CA125 level were all significant predictors of LNM. The nomogram established using the above variables had great performance for clinical applying.

scholarly journals Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sijia Cui ◽  
Tianyu Tang ◽  
Qiuming Su ◽  
Yajie Wang ◽  
Zhenyu Shu ◽  
...  
Keyword(s):  
Calibration Curve ◽  
Main Pancreatic Duct ◽  
Validation Cohort ◽  
External Validation ◽  
High Grade ◽  
Intraductal Papillary Mucinous Neoplasms ◽  
Training Cohort ◽  
Independent Validation ◽  
Medical Centers ◽  
Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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