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Nomograms to predict the presence and extent of inguinal lymph node metastasis in penile cancer patients with clinically positive lymph nodes.

ABSTRACT: Background:We developed two nomograms to predict the presence of inguinal lymph node metastasis (ILNM) and two or more histologically positive inguinal lymph nodes (ILNs) or one ILN with extranodal extension (ENE) in penile cancer patients with clinically positive inguinal lymph nodes (ILNs). Methods:This study enrolled 75 penile squamous cell carcinoma (PSCC) patients with clinically positive ILNs. A binary logistic regression model was taken to screen out the risk factors for the presence of ILNM and two or more positive ILNs or one node with ENE in one side. Nomograms were plotted based on the results of multivariate logistic regression analysis. Results:Our multivariate analysis demonstrated that the pathological grade, lymphovascular invasion (LVI), and short diameter (SD) of the largest ILN were independent risks. The bootstrap-corrected concordance index of the nomogram for the presence of ILNM was 0.948, and 0.878 for two or more histologically positive ILNs or one ILN with ENE on one side. Conclusions:By incorporating tumor pathological grade, LVI and SD of the largest ILN, our nomograms can efficiently predict the ILNM in penile cancer patients with clinically positive nodes.

SUBMITTER: Zhou X

PROVIDER: S-EPMC7215016 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Nomograms to predict the presence and extent of inguinal lymph node metastasis in penile cancer patients with clinically positive lymph nodes.

Zhou Xiang X Zhong Yan Y Song Lebin L Wang Yamin Y Wang Yichun Y Zhang Qijie Q Cong Rong R Ji Chengjian C Yu Tongfu T Song Ninghong N

Translational andrology and urology 20200401 2

<h4>Background</h4>We developed two nomograms to predict the presence of inguinal lymph node metastasis (ILNM) and two or more histologically positive inguinal lymph nodes (ILNs) or one ILN with extranodal extension (ENE) in penile cancer patients with clinically positive inguinal lymph nodes (ILNs).<h4>Methods</h4>This study enrolled 75 penile squamous cell carcinoma (PSCC) patients with clinically positive ILNs. A binary logistic regression model was taken to screen out the risk factors for th ...[more]

PMID: 32420168

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Project description:IntroductionThe axillary lymph node (ALN) status of breast cancer patients is an important prognostic indicator. The use of primary breast mass features for the prediction of ALN status is rare. Two nomograms based on preoperative ultrasound (US) images of breast tumors and ALNs were developed for the prediction of ALN status.MethodsA total of 743 breast cancer cases collected from 2016 to 2019 at the Second Affiliated Hospital of Harbin Medical University were randomly divided into a training set (n = 523) and a test set (n = 220). A primary tumor feature model (PTFM) and ALN feature model (ALNFM) were separately generated based on tumor features alone, and a combination of features was used for the prediction of ALN status. Logistic regression analysis was used to construct the nomograms. A receiver operating characteristic curve was plotted to obtain the area under the curve (AUC) to evaluate accuracy, and bias-corrected AUC values and calibration curves were obtained by bootstrap resampling for internal and external verification. Decision curve analysis was applied to assess the clinical utility of the models.ResultsThe AUCs of the PTFM were 0.69 and 0.67 for the training and test sets, respectively, and the bias-corrected AUCs of the PTFM were 0.67 and 0.67, respectively. Moreover, the AUCs of the ALNFM were 0.86 and 0.84, respectively, and the bias-corrected AUCs were 0.85 and 0.81, respectively. Compared with the PTFM, the ALNFM showed significantly improved prediction accuracy (p < 0.001). Both the calibration and decision curves of the ALNFM nomogram indicated greater accuracy and clinical practicality. When the US tumor size was ≤21.5 mm, the Spe was 0.96 and 0.92 in the training and test sets, respectively. When the US tumor size was greater than 21.5 mm, the Sen was 0.85 in the training set and 0.87 in the test set. Our further research showed that when the US tumor size was larger than 35 mm, the Sen was 0.90 in the training set and 0.93 in the test set.ConclusionThe ALNFM could effectively predict ALN status based on US images especially for different US tumor size.

Dataset Information

Nomograms to predict the presence and extent of inguinal lymph node metastasis in penile cancer patients with clinically positive lymph nodes.

Publications

Nomograms to predict the presence and extent of inguinal lymph node metastasis in penile cancer patients with clinically positive lymph nodes.

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