Impact of lymph node ratio and number of lymph node metastases on survival and recurrence in laryngeal squamous cell carcinoma

Defining a minimum nodal yield for neck dissection in mucosal head and neck squamous cell carcinoma, a systematic review

BACKGROUND

Analysis of existing literature on lymph node yield (LNY) in neck dissection (ND) for head and neck squamous cell carcinomas (HNSCC) used as a prognostic factor and an indication of treatment adequacy.

METHODS

PubMed, EMBASE and Web of Science databases were systematically searched from January 2010 to June 2023. Inclusion criteria encompassed studies on mucosal HNSCC patients undergoing ND with data on LNY and its association with survival outcomes. The quality assessment followed the REMARK guidelines.

RESULTS

Among 29 included studies, minimum LNY tresholds associated with improved survival outcomes ranged from 10 to 36.5 nodes. The heterogeneity in subsite involvement and cN0/cN + status constituted a challenge in establishing a consensus cutoff. The review highlights the need for standardized surgical techniques and pathological assessments to ensure data comparability.

CONCLUSIONS

LNY is a prognostic indicator and could reflect ND quality in HNSCC.

Integrating lymph node ratio into personalized radiotherapy for oral cavity squamous cell carcinoma

PURPOSE

The use of postoperative radiotherapy (PORT) in patients with oral squamous cell carcinoma (OCSCC) lacks clear boundaries due to the non-negligible toxicity accompanying its remarkable cancer-killing effect. This study aims at validating the ability of deep learning models to develop individualized PORT recommendations for patients with OCSCC and quantifying the impact of patient characteristics on treatment selection.

METHODS

Participants were categorized into two groups based on alignment between model-recommended and actual treatment regimens, with their overall survival compared. Inverse probability treatment weighting was used to reduce bias, and a mixed-effects multivariate linear regression illustrated how baseline characteristics influenced PORT selection.

RESULTS

4990 patients with OCSCC met the inclusion criteria. Deep Survival regression with Mixture Effects (DSME) demonstrated the best performance among all the models and National Comprehensive Cancer Network guidelines. The efficacy of PORT is enhanced as the lymph node ratio (LNR) increases. Similar enhancements in efficacy are observed in patients with advanced age, large tumors, multiple positive lymph nodes, tongue involvement, and stage IVA. Early-stage (stage 0-II) OCSCC may safely omit PORT.

CONCLUSIONS

This is the first study to incorporate LNR as a tumor character to make personalized recommendations for patients. DSME can effectively identify potential beneficiaries of PORT and provide quantifiable survival benefits.

Lymph node ratio-based model for predicting survival and assessing the benefit of adjuvant chemotherapy in postoperative duodenal adenocarcinoma

BACKGROUND

This study aimed to evaluate the prognostic significance of lymph node ratio in postoperative duodenal adenocarcinoma and develop a nomogram-based model for prognosis assessment and treatment optimization.

METHODS

Clinical information of patients with duodenal adenocarcinoma were retrieved from the Surveillance, Epidemiology, and End Results database, and prognostic factors were identified by univariate and multivariable analyses. Prognostic factors influencing patient outcomes were identified using univariate and multivariable Cox analyses. Subsequently, a novel nomogram and risk stratification system were developed based on these identified factors.

RESULTS

A total of 943 eligible patients were included, with 656 in the training cohort and 287 in the validation cohort. Lymph node ratio ≥0.12 were associated with poorer overall survival (hazard ratio 1.562, 95% confidence interval 1.195-2.041, and P = .001 for lymph node ratio = 0.12-0.30; hazard ratio 2.431, 95% confidence interval 1.847-3.199, and P < .001 for lymph node ratio >0.30). Prognostic factors including age at diagnosis, race, T stage, lymph node ratio, and tumor size were integrated into the nomogram. Patients in the low-risk group demonstrated significantly better overall survival compared with those in the high-risk group. Additionally, adjuvant chemotherapy significantly improved overall survival in the high-risk subgroup, whereas low-risk patients might be exempt from adjuvant chemotherapy.

CONCLUSIONS

This study represented the pioneering endeavor in introducing a lymph node ratio-based nomogram model for prognosis stratification and adjuvant chemotherapy decision-making protocol for patients with duodenal adenocarcinoma, thereby guiding personalized treatment strategies and minimizing overtreatment.

Development and validation of a machine learning model to predict the risk of lymph node metastasis in early-stage supraglottic laryngeal cancer

Background

Cervical lymph node metastasis (LNM) is a significant factor that leads to a poor prognosis in laryngeal cancer. Early-stage supraglottic laryngeal cancer (SGLC) is prone to LNM. However, research on risk factors for predicting cervical LNM in early-stage SGLC is limited. This study seeks to create and validate a predictive model through the application of machine learning (ML) algorithms.

Methods

The training set and internal validation set data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Data from 78 early-stage SGLC patients were collected from Fujian Provincial Hospital for independent external validation. We identified four variables associated with cervical LNM and developed six ML models based on these variables to predict LNM in early-stage SGLC patients.

Results

In the two cohorts, 167 (47.44%) and 26 (33.33%) patients experienced LNM, respectively. Age, T stage, grade, and tumor size were identified as independent predictors of LNM. All six ML models performed well, and in both internal and independent external validations, the eXtreme Gradient Boosting (XGB) model outperformed the other models, with AUC values of 0.87 and 0.80, respectively. The decision curve analysis demonstrated that the ML models have excellent clinical applicability.

Conclusions

Our study indicates that combining ML algorithms with clinical data can effectively predict LNM in patients diagnosed with early-stage SGLC. This is the first study to apply ML models in predicting LNM in early-stage SGLC patients.

Number of positive lymph nodes and lymph node ratio predict recurrence and survival in hypopharyngeal cancer based on SEER database and validation of real-world data

PURPOSE

This study investigated the impacts of the number of positive lymph nodes (NPLN) and lymph node ratio (LN ratio) for patients with hypopharyngeal squamous cell carcinoma (HPSCC) based on SEER database, which were validated in the real-world data of China.

METHODS

A total of 520 patients from SEER database were analyzed. Then 195 patients with pathologically stage III or IV HPSCC in our center were retrospectively studied.

RESULTS

In the SEER database, NPLN ≥ 3 was found in 36.9% of patients. Multivariate analysis revealed that LN ratio ≥ 0.138 was significant with poorer overall survival (OS) (hazard ratio [HR] = 1.525, p = 0.001) and cancer-specific survival (CSS) (HR = 1.697, p < 0.001), so was the NPLN ≥ 3 (HR = 1.388, p = 0.013; HR = 1.479, p = 0.008). Patients with NPLN ≥ 3 were found in 103 (52.8%) in our center. Multivariate analysis confirmed a significant association regarding OS (p = 0.005) or CSS (p = 0.003) between patients with LN ratio ≥ 0.138 or not. In addition, disease recurrence rate differed significantly between the patients with NPLN ≥ 3 (27.2%) and NPLN < 3 (14.1%, p = 0.026). Moreover, postoperative chemoradiotherapy (CCRT) was significantly associated with better prognosis in patients with NPLN ≥ 3.

CONCLUSION

In the SEER database, NPLN ≥ 3 and LN ratio ≥ 0.138 were independent poor prognostic factors for patients with HPSCC. Whereas identifying worldwide cut-off values for LN ratio is difficult and surgeon-dependent. In our cohort, adjuvant CCRT was beneficial for OS in patients with NPLN ≥ 3.

Thyroid cartilage infiltration in advanced laryngeal cancer: prognostic implications and predictive modelling

Objective

Detection of laryngeal cartilage invasion is of great importance in staging of laryngeal squamous cell carcinoma (LSCC). The role of prognosticators in locally advanced laryngeal cancer are still widely debated. This study aimed to assess the impact of volume of thyroid cartilage infiltration, as well as other histopathologic variables, on patient survival.

Materials and methods

We retrospectively analysed 74 patients affected by pT4 LSCC and treated with total laryngectomy between 2005 and 2021 at the Department of Otorhinolaryngology - Head and Neck Surgery of the University of Brescia, Italy. We considered as potential prognosticators histological grade, perineural (PNI) and lympho-vascular invasion (LVI), thyroid cartilage infiltration, and pTN staging. Pre-operative CT or MRI were analysed to quantify the volume of cartilage infiltration using 3D Slicer software.

Results

The 1-, 3-, and 5-year disease free survivals (DFS) were 76%, 66%, and 64%, respectively. Using machine learning models, we found that the volume of thyroid cartilage infiltration had high correlation with DFS. Patients with a higher volume (>670 mm3) of infiltration had a worse prognosis compared to those with a lower volume.

Conclusions

Our study confirms the essential role of LVI as prognosticator in advanced LSCC and, more innovatively, highlights the volume of thyroid cartilage infiltration as another promising prognostic factor.

Macroscopy of specimens from the head and neck

Macroscopic examination of surgical resections from the head and neck may be difficult due to the complex anatomy of this area. Recognition of normal anatomical structures is essential for accurate assessment of the extent of a disease process. Communication with the surgical team, correct specimen orientation and sampling are critical for assessment and the importance of radiological and clinical correlation is emphasised. Tumour involvement at each subsite is highlighted with reference to where there are implications on pathological staging and the potential need for adjuvant therapy.