Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review

BACKGROUND

In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting.

METHODS

A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases.

RESULTS

Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques.

CONCLUSION

Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.

Radiomics-Based Analysis in the Prediction of Occult Lymph Node Metastases in Patients with Oral Cancer: A Systematic Review

BACKGROUND

Tumor extension and metastatic cervical lymph nodes' (LNs) number and dimensions are major prognostic factors in patients with oral squamous cell carcinoma (OSCC). Radiomics-based models are being integrated into clinical practice in the prediction of LN status prior to surgery in order to optimize the treatment, yet their value is still debated.

METHODS

A systematic review of the literature was conducted according to the PRISMA guideline. Baseline study characteristics, and methodological items were extracted and summarized.

RESULTS

A total of 10 retrospective studies were included into the present study, each of them exploiting a single imaging modality. Data from a cohort of 1489 patients were analyzed: the highest AUC value was 99.5%, ACC ranges from 68% to 97.5%, and sensibility and specificity were over 0.65 and 0.70, respectively.

CONCLUSION

Radiomics may be a noninvasive tool to predict occult LN metastases (LNM) in OSCC patients prior to treatment; further prospective studies are warranted to create a reproducible and reliable method for the detection of LNM in OSCC.

The progress of radiomics in thyroid nodules

Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided.

Radiomics Features of Different Sizes of Medullary Thyroid Carcinoma (MTC) and Papillary Thyroid Carcinoma (PTC) Tumors: A Comparative Study

Background:

Radiomics strategies exhibit great promise in the context of thyroid nodule diagnosis. This study aimed to compare radiomics features of different sizes of medullary thyroid carcinoma (MTC) and papillary thyroid carcinoma (PTC) tumors and to compare the efficiency of radiomics approaches as a means of differentiating between these tumor types.

Methods:

In total, 86 MTC and 330 PTC nodules were divided into the macronodular (>10 mm) and micronodular (⩽10 mm) categories. The radiomics features of these nodules were analyzed to identify independent prognosis factors and evaluate the efficacy of individual and combined indicators as predictors of tumor type.

Results:

In total, 12 radiomics features were found to differ significantly between MTC and PTC macronodules, while 6 differed significantly between MTC and PTC micronodules. Shape 2D_Sphericity, firstorder_Skewness, glrlm_RunLengthNonUniformity, glszm_GrayLevelNonUniformity, and glszm_SizeZoneNonUniformity were features that were independently associated with the differential diagnoses of MTC and PTC macronodules. Receiver operating characteristic (ROC) curve analyses of the efficacy of these 5 single indicators and a combined indicator composed thereof yielded area under the curve (AUC) values of 0.621, 0.678, 0.704, 0.762, 0.747, and 0.824, respectively, with respective sensitivities of 55.3%, 43.0%, 53.1%, 56.3%, 46.9%, and 65.6%, and respective specificity values of 65.6%, 89.1%, 81.6%, 88.8%, 95.0%, and 91.1%. The glrlm_RunEntropy and glszm_SizeZoneNonUniformity features were identified as independent factors associated with the differential diagnoses of MTC and PTC micronodules. Receiver operating characteristic curve analyses of the efficacy of these 2 single indicators and a combined indicator composed thereof yielded respective AUC values of 0.678, 0.678, and 0.771; Sensitivities of 57.0%, 72.7%, and 72.7%; and specificities of 77.3%, 64.2%, and 77.5%.

Conclusions:

A range of different radiomics features can enable effective differentiation between MTC and PTC nodules of different sizes. Moreover, analyses of combinations of radiomics features yielded diagnostic efficiency values higher than those associated with single radiomics features, highlighting a more reliable approach to diagnosing MTC and PTC tumors.

Radiomics for Predicting Response of Neoadjuvant Chemotherapy in Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis

Purpose

This study examined the methodological quality of radiomics to predict the effectiveness of neoadjuvant chemotherapy in nasopharyngeal carcinoma (NPC). We performed a meta-analysis of radiomics studies evaluating the bias risk and treatment response estimation.

Methods

Our study was conducted through a literature review as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included radiomics-related papers, published prior to January 31, 2022, in our analysis to examine the effectiveness of neoadjuvant chemotherapy in NPC. The methodological quality was assessed using the radiomics quality score. The intra-class correlation coefficient (ICC) was employed to evaluate inter-reader reproducibility. The pooled area under the curve (AUC), pooled sensitivity, and pooled specificity were used to assess the ability of radiomics to predict response to neoadjuvant chemotherapy in NPC. Lastly, the Quality Assessment of Diagnostic Accuracy Studies technique was used to analyze the bias risk.

Results

A total of 12 studies were eligible for our systematic review, and 6 papers were included in our meta-analysis. The radiomics quality score was set from 7 to 21 (maximum score: 36). There was satisfactory ICC (ICC = 0.987, 95% CI: 0.957–0.996). The pooled sensitivity and specificity were 0.88 (95% CI: 0.71–0.95) and 0.82 (95% CI: 0.68–0.91), respectively. The overall AUC was 0.91 (95% CI: 0.88–0.93).

Conclusion

Prediction response of neoadjuvant chemotherapy in NPC using machine learning and radiomics is beneficial in improving standardization and methodological quality before applying it to clinical practice.