Dataset Information

Association of Lymphovascular Space Invasion With Locoregional Failure and Survival in Patients With Node-Negative Oral Tongue Cancers.

ABSTRACT: Importance:The indications for adjuvant therapy in resected oral tongue cancers are based on both clinical and pathological factors, with clear evidence for adjuvant radiation in patients with pathologically positive neck lymph nodes, positive margins, and extracapsular extension, but the data for patients with no nodal disease are sparse. Objective:To investigate determinants of failure and survival in patients with node-negative oral tongue cancer. Design, Setting, and Participants:Medical records for patients with oral tongue cancer treated with definitive surgery from 2003 to 2013 were reviewed. All patients were cN0 negative and classified as pathologically node-negative (pN0) if a neck dissection was performed. Patients received adjuvant radiotherapy (RT) or chemoradiotherapy (CRT) based on standard clinical and pathological determinants. Main Outcomes and Measures:Kaplan-Meier and multivariable (MVA) logistic regression and Cox proportional hazard regression analyses were performed to identify patient, tumor, and treatment characteristics predictive of locoregional control (LRC) and overall survival (OS). Results:A total of 180 patients met entry criteria, with a median follow-up time of 4.9 years (range, 0.9-12.5 years); 102 patients (56.7%) were female and 42 patients (23.3%) were younger than 45 years at diagnosis. One hundred fifty-three patients (85%) had T1/T2 tumors, and 112 patients (62%) had elective neck dissections with confirmed pN0. Lymphovascular space invasion (LVSI) was present in 36 patients (20%). On MVA, LVSI (OR, 0.06; 95% CI, 0.02-0.19; P?

SUBMITTER: Cassidy RJ

PROVIDER: S-EPMC5398912 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Association of Lymphovascular Space Invasion With Locoregional Failure and Survival in Patients With Node-Negative Oral Tongue Cancers.

Cassidy Richard J RJ Switchenko Jeffrey M JM Jegadeesh Naresh N Sayan Mutlay M Ferris Matthew J MJ Eaton Bree R BR Higgins Kristin A KA Wadsworth Jeffrey T JT Magliocca Kelly R KR Saba Nabil F NF Beitler Jonathan J JJ

JAMA otolaryngology-- head & neck surgery 20170401 4

<h4>Importance</h4>The indications for adjuvant therapy in resected oral tongue cancers are based on both clinical and pathological factors, with clear evidence for adjuvant radiation in patients with pathologically positive neck lymph nodes, positive margins, and extracapsular extension, but the data for patients with no nodal disease are sparse.<h4>Objective</h4>To investigate determinants of failure and survival in patients with node-negative oral tongue cancer.<h4>Design, setting, and partic ...[more]

PMID: 28097311

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Project description:To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

Dataset Information

Association of Lymphovascular Space Invasion With Locoregional Failure and Survival in Patients With Node-Negative Oral Tongue Cancers.

Publications

Association of Lymphovascular Space Invasion With Locoregional Failure and Survival in Patients With Node-Negative Oral Tongue Cancers.

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