Dataset Information

A model to predict nodal metastasis in patients with oral squamous cell carcinoma.

ABSTRACT: Difficulty in precise decision making on necessity of surgery is a major problem when managing oral squamous cell carcinomas (OSCCs) with clinically negative neck. Therefore, use of clinical and histopathological parameters in combination would be important to improve patient management. The main objective is to develop a model that predicts the presence of nodal metastasis in patients with OSCC.623 patients faced neck dissections with buccal mucosal or tongue squamous cell carcinoma (SCC) were selected from patients' records. Demographic data, clinical information, nodal status, Depth of invasion (DOI) and pattern of invasion (POI) were recorded. The parameters which showed a significant association with nodal metastasis were used to develop a multivariable predictive model (PM). Univariate logistic regression was used to estimate the strengths of those associations in terms of odds ratios (OR). This showed statistically significant associations between status of the nodal metastasis and each of the following 4 histopathological parameters individually: size of the tumour (T), site, POI, and DOI. Specifically, OR of nodal metastasis for tongue cancers relative to buccal mucosal cancers was 1.89, P-value < 0.001. Similarly, ORs for POI type 3 and 4 relative to type 2 were 1.99 and 5.83 respectively. A similar relationship was found with tumour size; ORs for T2, T3, and T4 compared to T1 were 2.79, 8.27 and 8.75 respectively. These four histopathological parameters were then used to develop a predictive model for nodal metastasis. This model showed that probability of nodal metastasis is higher among tongue cancers with increasing POI, with increasing T, and with larger depths while other characteristics remained unchanged. The proposed model provides a way of using combinations of histopathological parameters to identify patients with higher risks of nodal metastasis for surgical management.

SUBMITTER: De Silva RK

PROVIDER: S-EPMC6084951 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundEarly-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.Methods and findingsTumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.ConclusionsOur findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.

Project description:Oral squamous cell carcinoma (OSCC) is a major concern with high morbidity and mortality worldwide, even with the current knowledge and the advancement in treatment. OSCCs diagnosed at late-stage often require wide-excision with or without neck dissection, radiotherapy, or chemotherapy. When deemed successful, treatment often results in diminished quality of life, impaired function, and disfigurement. Strategies for early detection are urgently needed for patients afflicted with this disease. Inflammatory protein plasma biomarkers have shown to be potential tests for early detection and disease monitoring in several cancers. There has been no study on inflammation-related plasma biomarkers in OSCC. The objectives of the study were to use a multiplex approach to screen plasma-derived biomarkers and to examine the association of measurable proteins with OSCC. A total of 260 plasma samples (210 OSCC and 50 normal controls) were collected to measure for concentration of inflammatory related biomarkers using electrochemiluminescence multiplex assay. After screening of 82 potential biomarkers of the first 160 OSCC, 16 cytokines, chemokines, and growth factors were identified and verified in the second set of samples containing 50 OSCC and 50 normal. After adjustment of age and batch effects, the adjusted differential expression analysis showed that the OSCCs were markedly lower in 14 biomarkers and significantly higher level of interleukin 1 receptor antagonist (IL1Ra). By performing unsupervised clustering analysis, we observed distinctive groups of normal and two subgroups of OSCC. Linear regression of IL2, IL1Ra, and macrophage inhibitory factor (MIF) showed high accuracy in classifying OSCC with sensitivity of 0.96 and specificity of 0.92. In conclusion, this is the first paper to identify potential inflammatory plasma protein biomarkers of patients with OSCC. With further validation, the set of biomarkers can potentially be used to assist in early detection of OSCC when the disease is localized and in more treatable stage.

Project description:BackgroundLack of adequate objectivity and universality, available models are still difficult to be applied to clinical practice in predicting occult cervical metastasis of early oral squamous cell carcinoma (OSCC). Taking abnormal metabolic state into consideration, the current model is helpful to distinguish those patients with or without occult cervical metastasis.MethodsThis study retrospectively analyzed 330 OSCC patients initially diagnosed cT1-2N0M0 stage and received neck dissection from January 2020 to July 2022. The occult cervical metastasis was identified by pathological examination.. After screening independent risk factors using logistic regression, patients were divided into training and validation cohorts at the ratio of 2:1 randomly, and a novel diagnostic model was constructed. Performances of this model were evaluated by the area under the curve (AUC), calibrating curve, decision curve analysis (DCA) and clinical impact curve (CIC).ResultsOf the 330 included patients {age mean [standard deviation (SD)], 61.24 (12.99) years; 202 (61.2%) males}, 49 (14.8%) had occult nodal metastasis. Five variables, including body mass index (BMI) [high odds ratio (OR): 1.132; 95% confidence interval (CI): 1.019-1.258, P=0.021], primary tumor site (tongue & floor of mouth (TF) OR: 3.756; 95% CI: 1.295-10.898, P=0.015), depth of invasion (DOI) (5-10 mm OR: 2.973; 95% CI: 1.266-6.981; P=0.012), pathological differentiation (Poor differentiation OR: 2.65; 95% CI: 1.341-5.239; P=0.005), and diabetes (OR: 3.123; 95% CI: 1.23-7.929; P=0.017) were screened to establish the predictive model. In training cohort (n=220), this model achieved an AUC of 0.814 and had a sensitivity of 78.1% and specificity of 70.2%. Calibration plots showed favorable consistency between the prediction of the model and actual observations (Hosmer-Lemeshow value >0.05). Decision curve analysis (DCA) and clinical impact curve (CIC) showed the model was clinically useful and had better discriminative ability under the threshold probability of 0.5. Above evaluations were verified in the validation cohort (n=110). Compared to previous reported models, the concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI) values were superior in both training and validation cohorts (P<0.05).ConclusionsThis constructed model might have reference value for clinicians in making neck management decisions of early OSCC patients.

Dataset Information

A model to predict nodal metastasis in patients with oral squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets