MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model

OBJECTIVES

Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI).

MATERIALS-METHODS

MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds.

RESULTS

The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy.

CONCLUSIONS

This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.

Intratumoral and peritumoral radiomics of MRIs predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma

BACKGROUND

For patients with locally advanced head and neck squamous cell carcinoma (HNSCC), combined programmed death receptor-1 inhibitor and chemotherapy improved response rate to neoadjuvant therapy. However, treatment response varies among patients. There is no tool to predict pathologic complete response (pCR) with high accuracy for now. To develop a tool based on radiomics features of MRI to predict pCR to neoadjuvant chemoimmunotherapy (NACI) may provide valuable assistance in treatment regimen determination for HNSCC.

METHODS

From January 2021 to April 2024, a total of 172 patients with HNSCC from three medical center, who received NACI followed by surgery, were included and allocated into a training set (n=84), an internal validation set (n=37) and an external validation set (n=51). Radiomics features were extracted from intratumoral and different peritumoral areas, and radiomics signature (Rad-score) for each area was constructed. A radiomics-clinical nomogram was developed based on Rad-scores and clinicopathological characteristics, tested in the validation sets, and compared with clinical nomogram and combined positive score (CPS) in predicting pCR.

RESULTS

The radiomics-clinical nomogram, incorporating peritumoral Rad-score, intratumoral Rad-score and CPS, achieved the highest accuracy with areas under the receiver operating characteristic curve of 0.904 (95% CI, 0.835 to 0.972) in the training cohort, 0.860 (95% CI, 0.722 to 0.998) in the internal validation cohort, and 0.849 (95% CI, 0.739 to 0.959) in the external validation cohort, respectively, which outperformed the clinical nomogram and CPS in predict pCR to NACI for HNSCC.

CONCLUSION

A nomogram developed based on intratumoral and peritumoral MRI radiomics features outperformed CPS, a widely employed biomarker, in predict pCR to NACI for HNSCC, which would provide incremental value in treatment regimen determination.

Imaging Modalities for Head and Neck Cancer: Present and Future

Several imaging modalities are utilized in the diagnosis, treatment, and surveillance of head and neck cancer. First-line imaging remains computed tomography (CT); however, MRI, PET with CT (PET/CT), and ultrasound are often used. In the last decade, several new imaging modalities have been developed that have the potential to improve early detection, modify treatment, decrease treatment morbidity, and augment surveillance. Among these, molecular imaging, lymph node mapping, and adjustments to endoscopic techniques are promising. The present review focuses on existing imaging, novel techniques, and the recent changes to imaging practices within the field.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study

PURPOSE

Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).

MATERIALS AND METHODS

Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models.

RESULTS

We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively.

CONCLUSION

Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis

The aim of this work is to identify MRI texture features able to predict the response to radio-chemotherapy (RT-CHT) in patients with naso-oropharyngeal carcinoma (NPC-OPC) before treatment in order to help clinical decision making. Textural features were derived from ADC maps and post-gadolinium T1-images on a single MRI machine for 37 patients with NPC-OPC. Patients were divided into two groups (responders/non-responders) according to results from MRI scans and 18F-FDG-PET/CT performed at follow-up 3-4 and 12 months after therapy and biopsy. Pre-RT-CHT lesions were segmented, and radiomic features were extracted. A non-parametric Mann-Whitney test was performed. A p-value < 0.05 was considered significant. Receiver operating characteristic curves and area-under-the-curve values were generated; a 95% confidence interval (CI) was reported. A radiomic model was constructed using the LASSO algorithm. After feature selection on MRI T1 post-contrast sequences, six features were statistically significant: gldm_DependenceEntropy and DependenceNonUniformity, glrlm_RunEntropy and RunLengthNonUniformity, and glszm_SizeZoneNonUniformity and ZoneEntropy, with significant cut-off values between responder and non-responder group. With the LASSO algorithm, the radiomic model showed an AUC of 0.89 and 95% CI: 0.78-0.99. In ADC, five features were selected with an AUC of 0.84 and 95% CI: 0.68-1. Texture analysis on post-gadolinium T1-images and ADC maps could potentially predict response to therapy in patients with NPC-OPC who will undergo exclusive treatment with RT-CHT, being, therefore, a useful tool in therapeutical-clinical decision making.

MRI radiomics in head and neck cancer from reproducibility to combined approaches

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes

This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step approach: in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

This study addresses the limited non-invasive tools for Oral Cavity Squamous Cell Carcinoma (OSCC) survival prediction by identifying Computed Tomography (CT)-based biomarkers to improve prognosis prediction. A retrospective analysis was conducted on data from 149 OSCC patients, including CT radiomics and clinical information. An ensemble approach involving correlation analysis, score screening, and the Sparse-L1 algorithm was used to select functional features, which were then used to build Cox Proportional Hazards models (CPH). Our CPH achieved a 0.70 concordance index in testing. The model identified two CT-based radiomics features, Gradient-Neighboring-Gray-Tone-Difference-Matrix-Strength (GNS) and normalized-Wavelet-LLL-Gray-Level-Dependence-Matrix-Large-Dependence-High-Gray-Level-Emphasis (HLE), as well as stage and alcohol usage, as survival biomarkers. The GNS group with values above 14 showed a hazard ratio of 0.12 and a 3-year survival rate of about 90%. Conversely, the GNS group with values less than or equal to 14 had a 49% survival rate. For normalized HLE, the high-end group (HLE > - 0.415) had a hazard ratio of 2.41, resulting in a 3-year survival rate of 70%, while the low-end group (HLE ≤ - 0.415) had a 36% survival rate. These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for patients people with OSCC to improve their survival.

A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas

BACKGROUND

We recently developed a gene-expression-based HOT score to identify the hot/cold phenotype of head and neck squamous cell carcinomas (HNSCCs), which is associated with the response to immunotherapy. Our goal was to determine whether radiomic profiling from computed tomography (CT) scans can distinguish hot and cold HNSCC.

METHOD

We included 113 patients from The Cancer Genome Atlas (TCGA) and 20 patients from the Groupe Hospitalier Pitié-Salpêtrière (GHPS) with HNSCC, all with available pre-treatment CT scans. The hot/cold phenotype was computed for all patients using the HOT score. The IBEX software (version 4.11.9, accessed on 30 march 2020) was used to extract radiomic features from the delineated tumor region in both datasets, and the intraclass correlation coefficient (ICC) was computed to select robust features. Machine learning classifier models were trained and tested in the TCGA dataset and validated using the area under the receiver operator characteristic curve (AUC) in the GHPS cohort.

RESULTS

A total of 144 radiomic features with an ICC >0.9 was selected. An XGBoost model including these selected features showed the best performance prediction of the hot/cold phenotype with AUC = 0.86 in the GHPS validation dataset.

CONCLUSIONS AND RELEVANCE

We identified a relevant radiomic model to capture the overall hot/cold phenotype of HNSCC. This non-invasive approach could help with the identification of patients with HNSCC who may benefit from immunotherapy.

Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma

Purpose

To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).

Materials and methods

108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.

Results

MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.

Conclusion

MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.