Intraoperative frozen section for detection of occult metastasis in clinically N0 neck does not improve outcome in oral cavity carcinomas

Deep learning-based hyperspectral technique identifies metastatic lymph nodes in oral squamous cell carcinoma-A pilot study

AIMS

To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.

MAIN METHODS

The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue.

KEY FINDINGS

It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively.

SIGNIFICANCE

This study indicated that deep learning-based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample.

FD-Net: Feature Distillation Network for Oral Squamous Cell Carcinoma Lymph Node Segmentation in Hyperspectral Imagery

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical lymph metastases. Therefore, it is necessary to rely on a reasonable screening method to quickly judge the cervical lymph metastastic condition of OSCC patients and develop appropriate treatment plans. In this study, the widely used pathological sections with hematoxylin-eosin (H&E) staining are taken as the target, and combined with the advantages of hyperspectral imaging technology, a novel diagnostic method for identifying OSCC lymph node metastases is proposed. The method consists of a learning stage and a decision-making stage, focusing on cancer and non-cancer nuclei, gradually completing the lesions' segmentation from coarse to fine, and achieving high accuracy. In the learning stage, the proposed feature distillation-Net (FD-Net) network is developed to segment the cancerous and non-cancerous nuclei. In the decision-making stage, the segmentation results are post-processed, and the lesions are effectively distinguished based on the prior. Experimental results demonstrate that the proposed FD-Net is very competitive in the OSCC hyperspectral medical image segmentation task. The proposed FD-Net method performs best on the seven segmentation evaluation indicators: MIoU, OA, AA, SE, CSI, GDR, and DICE. Among these seven evaluation indicators, the proposed FD-Net method is 1.75%, 1.27%, 0.35%, 1.9%, 0.88%, 4.45%, and 1.98% higher than the DeepLab V3 method, which ranks second in performance, respectively. In addition, the proposed diagnosis method of OSCC lymph node metastasis can effectively assist pathologists in disease screening and reduce the workload of pathologists.

Tumor-stroma ratio is a crucial histological predictor of occult cervical lymph node metastasis and survival in early-stage (cT1/2N0) oral squamous cell carcinoma

Occult cervical lymph node metastasis is a significant prognostic factor in patients with early-stage (cT1/2N0) oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the potential value of the tumor-stroma ratio (TSR) as a histological predictor of occult cervical metastasis and survival in early-stage OSCC. This retrospective study included 151 patients who underwent excision of the primary lesion and elective neck dissection from 2013 to 2017. The clinicopathological features of the tumor, risk factors associated with occult neck metastasis, and prognostic factors for overall survival (OS) and disease-free survival (DFS) were studied. A significant correlation of TSR (P = 0.009) was found with occult neck metastasis in the multivariate logistic regression model. Multivariate Cox proportional hazards regression analysis showed that the TSR (P = 0.002) and perineural invasion (P = 0.011) were associated with OS. Occult neck metastasis (P = 0.032) was associated with DFS. These findings indicate that assessment of the TSR might be useful in prognostication for early-stage OSCC patients. Moreover, the TSR is effective in allowing an accurate evaluation of the risk of occult neck metastasis, and this may be easily applicable in the routine pathological diagnosis and clinical decision-making for elective neck dissection.