Development and validation of a nomogram for prognosis of sinonasal squamous cell carcinoma

Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram

RATIONALE AND OBJECTIVES

Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) based on deep learning and multiparametric MRI for the risk of 2-year recurrence in advanced SNSCC.

MATERIALS AND METHODS

Preoperative MRI datasets were retrospectively collected from 265 SNSCC patients (145 recurrences) who underwent preoperative MRI, including T2-weighted (T2W), contrast-enhanced T1-weighted (T1c) sequences and diffusion-weighted (DW). All patients were divided into 165 training cohort and 70 test cohort. A deep learning segmentation model based on VB-Net was used to segment regions of interest (ROIs) for preoperative MRI and radiomics features were extracted from automatically segmented ROIs. Least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) were applied for feature selection and radiomics score construction. Combined with meaningful clinicopathological predictors, a nomogram was developed and its performance was evaluated. In addition, X-title software was used to divide patients into high-risk or low-risk early relapse (ER) subgroups. Recurrence-free survival probability (RFS) was assessed for each subgroup.

RESULTS

The radiomics score, T stage, histological grade and Ki-67 predictors were independent predictors. The segmentation models of T2WI, T1c, and apparent diffusion coefficient (ADC) sequences achieved Dice coefficients of 0.720, 0.727, and 0.756, respectively, in the test cohort. RS-T2, RS-T1c and RS-ADC were derived from single-parameter MRI. RS-Combined (combined with T2WI, T1c, and ADC features) was derived from multiparametric MRI and reached area under curve (AUC) and accuracy of 0.854 (0.749-0.927) and 74.3% (0.624-0.840), respectively, in the test cohort. The calibration curve and decision curve analysis (DCA) illustrate its value in clinical practice. Kaplan-Meier analysis showed that the 2-year RFS rate for low-risk patients was significantly greater than that for high-risk patients in both the training and testing cohorts (p < 0.001).

CONCLUSION

Automated nomograms based on multi-sequence MRI help to predict ER in SNSCC patients preoperatively.

Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study

PURPOSE

Accurate histologic grade assessment is helpful for clinical decision making and prognostic assessment of sinonasal squamous cell carcinoma (SNSCC). This research aimed to explore whether whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps with machine learning algorithms can predict histologic grade of SNSCC.

METHODS

One hundred and forty-seven patients with pathologically diagnosed SNSCC formed this retrospective study. Sixty-six patients were low-grade (grade I/II) and eighty-one patients were high-grade (grade III). Eighteen histogram features were obtained from quantitative ADC maps. Additionally, the mean ADC value and clinical features were analyzed for comparison with histogram features. Machine learning algorithms were applied to build the best diagnostic model for predicting histological grade. The receiver operating characteristic (ROC) curve was used to evaluate the performance of each model prediction, and the area under the ROC curve (AUC) were analyzed.

RESULTS

The histogram model based on three features (10th Percentile, Mean, and 90th Percentile) with support vector machine (SVM) classifier demonstrated excellent diagnostic performance, with an AUC of 0.947 on the testing dataset. The AUC of the histogram model was similar to that of the mean ADC value model (0.947 vs 0.957; P = 0.7029). The poor diagnostic performance of the clinical model (AUC = 0.692) was improved by the combined model incorporating histogram features or mean ADC value (P < 0.05).

CONCLUSION

ADC histogram analysis improved the projection of SNSCC histologic grade, compared with clinical model. The complex histogram model had comparable but not better performance than mean ADC value model.

An MRI-Based Radiomics Nomogram to Assess Recurrence Risk in Sinonasal Malignant Tumors

BACKGROUND

Sinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem.

PURPOSE

To establish an MRI-based radiomics nomogram for assessing relapse risk in patients with SNMT.

STUDY TYPE

Retrospective.

POPULATION

A total of 143 patients with 68.5% females (development/validation set, 98/45 patients).

FIELD STRENGTH/SEQUENCE

A 1.5-T and 3-T, fat-suppressed fast spin echo (FSE) T2-weighted imaging (FS-T2WI), FSE T1-weighted imaging (T1WI), and FSE contrast-enhanced T1WI (T1WI + C).

ASSESSMENT

Three MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS-T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high-dimensional features. The clinical model was built based on clinical and MRI features. The Ki-67-based and tumor-node-metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse-free survival analysis was used among 143 patients.

STATISTICAL TESTS

The intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan-Meier curve, log-rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant.

RESULTS

The T1 + C-based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS-T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki-67-based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated.

DATA CONCLUSION

A radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki-67-based model, and TNM model.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 5.

Prognostic role of baseline 18 F-FDG pet/CT in squamous cell carcinoma of the paranasal sinuses

BACKGROUND

To retrospectively investigate the prognostic role of baseline ¹⁸ F-FDG PET/CT in squamous cell carcinoma (SCC) of the paranasal sinuses.

METHODS

Clinical features and PET/CT semiquantitative parameters of 49 patients were collected. Anova and Kruskall-Wallis tests were used to assess the relationship between these parameters. Kaplan-Meier, univariate, and multivariate analysis were performed to search for independent prognostic factors for progression free (PFS) and overall survival (OS).

RESULTS

Mean PFS was 29.95 months (SD 29.36) with relapse/progression of disease occurring in 18 patients; mean OS was 33.40 (SD 27.78) months with death occurring in 15 patients. Presence of nodal metastasis (14 subjects) was correlated with standardize uptake value (SUV) max, SUVmean, SUV/blood-pool ratio, SUV/liver ratio, metabolic tumor volume, and total lesion glycolysis. SUVmax, SUVmean, and presence of nodal metastasis resulted as independent prognostic factors for OS.

CONCLUSION

¹⁸ F-FDG PET/CT semiquantitative parameters confirmed their prognostic role for SCC of paranasal sinuses.

Development and validation of a nomogram for prognosis of sinonasal adenocarcinoma (a nomogram for sinonasal adenocarcinoma)

BACKGROUND

The incidence of sinonasal adenocarcinoma is low, and there are few studies on survival and prognosis. Therefore, we aim to develop and validate a prognostic model for predicting the overall survival of sinonasal adenocarcinoma and provide guidance for clinical management.

METHODS

Patients who were diagnosed as sinonasal adenocarcinoma through Surveillance, Epidemiology, and End Results database between 1975 and 2015 were randomly divided into a training group and validation group. Univariate, multivariate survival analysis was performed to screen independent survival factors. A nomogram was established to predict the overall survival rate of sinonasal adenocarcinoma. Receiver operating characteristic curve and calibration plot were performed to verify the discrimination and accuracy of the model. A decision curve analysis was performed to verify the clinical applicability of the model.

RESULTS

A total of 423 patients with sinonasal adenocarcinoma were randomly divided into training group (n = 299) and verification group (n = 124). We established and verified the Nomo map including age, marriage, grade, surgery and tumour size. The c-index of Surveillance, Epidemiology, and End Results stage, T stage and this model are 0.635, 0.626 and 0.803, respectively. The survival rate of the high-risk group scored by this model was lower than that of the low-risk group (P < 0.001). Decision curve analysis shows that the model has advantages in predicting survival rates.

CONCLUSION

Our model is considered to be a useful tool for predicting the overall survival of sinonasal adenocarcinoma, with good discrimination and clinical applicability. We hope that this model will help rhinologists to make clinical decisions and manage patients diagnosed with sinonasal adenocarcinoma.

A Competing Risk Nomogram for Predicting Cancer-Specific Death of Patients With Maxillary Sinus Carcinoma

Objectives

Herein, we purposed to establish and verify a competing risk nomogram for estimating the risk of cancer-specific death (CSD) in Maxillary Sinus Carcinoma (MSC) patients.

Methods

The data of individuals with MSC used in this study was abstracted from the (SEER) Surveillance, Epidemiology, and End Results data resource as well as from the First Affiliated Hospital of Nanchang University (China). The risk predictors linked to CSD were identified using the CIF (cumulative incidence function) along with the Fine-Gray proportional hazards model on the basis of univariate analysis coupled with multivariate analysis implemented in the R-software. After that, a nomogram was created and verified to estimate the three- and five-year CSD probability.

Results

Overall, 478 individuals with MSC were enrolled from the SEER data resource, with a 3- and 5-year cumulative incidence of CSD after diagnosis of 42.1% and 44.3%, respectively. The Fine-Gray analysis illustrated that age, histological type, N stage, grade, surgery, and T stage were independent predictors linked to CSD in the SEER-training data set (n = 343). These variables were incorporated in the prediction nomogram. The nomogram was well calibrated and it demonstrated a remarkable estimation accuracy in the internal validation data set (n = 135) abstracted from the SEER data resource and the external validation data set (n = 200). The nomograms were well-calibrated and had a good discriminative ability with concordance indexes (c-indexes) of 0.810, 0.761, and 0.755 for the 3- and 5-year prognosis prediction of MSC-specific mortality in the training cohort, internal validation, and external validation cohort, respectively.

Conclusions

The competing risk nomogram constructed herein proved to be an optimal assistant tool for estimating CSD in individuals with MSC.

Malignant Sinonasal Tumors: Update on Histological and Clinical Management

Tumors of nasal cavity and paranasal sinuses (TuNSs) are rare and heterogeneous malignancies, presenting different histological features and clinical behavior. We reviewed the literature about etiology, biology, and clinical features of TuNSs to define pathologic features and possible treatment strategies. From a diagnostic point of view, it is mandatory to have high expertise and perform an immunohistochemical assessment to distinguish between different histotypes. Due to the extreme rarity of these neoplasms, there are no standard and evidence-based therapeutic strategies, lacking prospective and large clinical trials. In fact, most studies are retrospective analyses. Surgery represents the mainstay of treatment of TuNSs for small and localized tumors allowing complete tumor removal. Locally advanced lesions require more demolitive surgery that should be always followed by adjuvant radio- or chemo-radiotherapy. Recurrent/metastatic disease requires palliative chemo- and/or radiotherapy. Many studies emphasize the role of specific genes mutations in the development of TuNSs like mutations in the exons 4-9 of the TP53 gene, in the exon 9 of the PIK3CA gene and in the promoter of the TERT gene. In the near future, this genetic assessment will have new therapeutic implications. Future improvements in the understanding of the etiology, biology, and clinical features of TuNSs are warranted to improve their management.