Dataset Information

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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Publications

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:BackgroundPenile squamous cell carcinoma is a relatively rare malignancy among male malignancies, there are more than 30,000 new cases and more than 10,000 deaths of penile cancer annually. In patients with penile malignancy, inguinal lymph node metastasis (ILNM) significantly reduces patient survival. Thus, we identified the risk factors for ILNM in penile malignancies, aiming to develop a precise prediction model.MethodsWe retrospectively analyzed 112 male patients with penile cancer. All subjects underwent penile surgery and inguinal lymphadenectomy at the same time, and postoperative pathology confirmed ILNM. Fisher's exact test, t-test, and Wilcoxon rank sum test were used to assess differences in demographic information and clinical features between the two groups, followed by logical least absolute shrinkage and selection operator (LASSO) regression analysis to determine risk factors of ILNM. The prediction model was constructed using nomogram.ResultsLASSO regression revealed that age [β=-0.005, odds ratio (OR) =0.995], smoking history (β=-0.006, OR =0.994) and interleukin 2 (IL-2) level (β=-0.0112, OR =0.989) were protective against ILNM. However, lymph node diameter (β=0.3117, OR =1.366), T-stage (β=0.1254, OR =1.134), fibrinogen (β=0.0377, OR =1.038), IL-4 level (β=0.004, OR =1.001), and neutrophil-to-lymphocyte ratio (β=0.0355, OR =1.034) were risk factors for developing ILNM. When assessing the risk of metastasis, it is crucial to balance these factors. The aforementioned characteristics were utilized to establish the predictive model, which demonstrated a good predictive ability with an area under the curve (AUC) value of 0.81. Moreover, internal leave-one-way cross-validation was used to construct a nomogram showing consistency, with an AUC of 0.75.ConclusionsThe diagnosis of ILNM in penile malignant tumors can be predicted through clinicopathological features, biochemical tests, and prediction models based on tumor markers.

Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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