Dataset Information

Relation between the level of lymph node metastasis and survival in locally advanced head and neck squamous cell carcinoma.

ABSTRACT: BACKGROUND:The current head and neck squamous cell carcinoma (HNSCC) staging system may not capture the full prognostic implications of regional lymph node involvement. This study investigated the impact of the level of lymph node metastasis (LNM) on survival. METHODS:The Surveillance, Epidemiology, and End Results registry was queried for oral cavity (OC), oropharynx (OP), larynx (LAR), and hypopharynx (HP) HNSCC. A multivariate Cox proportional hazards model was used to evaluate whether the level of LNM was an independent prognostic factor. Site-specific recursive-partitioning analysis was performed to classify patients into different risk groups. RESULTS:In all, 14,499 patients, including OC (n?=?2463), OP (n?=?8567), LAR (n?=?2332), and HP patients (n?=?1137), were analyzed. Both the American Joint Committee on Cancer (AJCC) N classification and the level of LNM showed significant effects on overall survival (OS) in patients with OC, OP, or LAR HNSCC but not in patients with HP HNSCC. In patients with N2 disease, the AJCC subclassification (N2a, N2b, or N2c) was significantly associated with the OS of patients with OP and LAR HNSCC but not with the OS of patients with OC or HP HNSCC, whereas the level of LNM (primary, secondary, or tertiary) was significantly associated with the OS of patients with OC, OP, and LAR HNSCC but not HP HNSCC. With recursive-partitioning analysis, a simple, primary site-specific prognostic tool integrating the AJCC T and N classifications and the level of LNM was designed, and it could be easily used by health care providers in clinic. CONCLUSIONS:The level of LNM is an independent prognostic factor for patients with locally advanced HNSCC and could add to the prognostic value of AJCC T and N classifications in patients with locally advanced HNSCC.

SUBMITTER: Xing Y

PROVIDER: S-EPMC4742373 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Relation between the level of lymph node metastasis and survival in locally advanced head and neck squamous cell carcinoma.

Xing Yan Y Zhang Jianjun J Lin Heather H Gold Kathryn A KA Sturgis Erich M EM Garden Adam S AS Lee J Jack JJ William William N WN

Cancer 20151110 4

<h4>Background</h4>The current head and neck squamous cell carcinoma (HNSCC) staging system may not capture the full prognostic implications of regional lymph node involvement. This study investigated the impact of the level of lymph node metastasis (LNM) on survival.<h4>Methods</h4>The Surveillance, Epidemiology, and End Results registry was queried for oral cavity (OC), oropharynx (OP), larynx (LAR), and hypopharynx (HP) HNSCC. A multivariate Cox proportional hazards model was used to evaluate ...[more]

PMID: 26554754

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Project description:Objective:To investigate the prevalence and distribution of cervical lymph node metastasis (LNM) in locally advanced supraglottic squamous cell carcinoma (LASCC) and guide the delineation of clinical lymph node target volumes. Materials and Methods:We reviewed patients defined as LASCC from January 2000 to December 2017 in our hospital. The primary tumor was operated on using partial or total laryngectomy, and all patients underwent bilateral neck dissection (levels II-IV at least). Univariate and multivariate logistic regressions were used to find risk factors associated with LNM. Results:A total of 206 patients were enrolled. In the whole group, the rate of ipsilateral metastasis (IM) was 60.9% (67 patients), whereas contralateral metastasis was 25.5% (28 patients). Only positive ipsilateral lymph nodes contributed to contralateral metastasis (p = 0.001). Seventy-six cases were diagnosed with clinical positive lymph nodes (cN+). IM of primary lesions mainly located within the unilateral sites (n = 49 patients) was detected in levels II, III, and IV with lymph node metastasis ratios of 73.5% (36 patients), 63.3% (31 patients), and 20.4% (10 patients), respectively, and contralateral metastasis of 36.7% (18 patients), 16.3% (8 patients), and 6.1% (3 patients), respectively. Involvement of level II or III was associated with metastasis of level IV. No one developed contralateral level IV involvement without metastasis of contralateral levels II and III. A total of 130 cases had clinically negative neck lymph nodes (cN0). The prevalence of occult metastasis (OM) was 35.4%. Among 62 patients with unilateral lesions, the rates of OM to ipsilateral neck levels II, III, and IV were 21, 11.1, and 1.6%, respectively, whereas contralateral neck levels were 6.3, 4.8, and 0%, respectively. In terms of the risk factors, histopathological differentiation was related to OM (p = 0.003). Two of 25 people were with level VIb metastasis, and both of them were with subglottic involvement. Conclusion:Neck levels II to IV are most frequently involved and should be included in clinical target volume (CTV) in cN+ patients. Contralateral IV may be omitted when contralateral levels II and III are negative. In cN0 patients, ipsilateral levels II and III are suggested to be included in the CTV, whereas whether contralateral levels II and III should be included needs further research.

Project description:BackgroundDeep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting.MethodsAn expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated.ResultsBlinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703).ConclusionsWe show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

Dataset Information

Relation between the level of lymph node metastasis and survival in locally advanced head and neck squamous cell carcinoma.

Publications

Relation between the level of lymph node metastasis and survival in locally advanced head and neck squamous cell carcinoma.

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