Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Gul, Maryam; Bonjoc, Kimberley-Jane C.; Gorlin, David; Wong, Chi Wah; Salem, Amirah; La, Vincent; Filippov, Aleksandr; Chaudhry, Abbas; Imam, Muhammad; Chaudhry, Ammar

doi:10.3389/fonc.2021.639326

Cited by 14 publications

(12 citation statements)

References 108 publications

(219 reference statements)

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“…All included articles were categorized using the image-mining study system and graded using the Radiomics Quality Scoring (RQS) system. In order to assist clinicians in medical decision-making, radiomics, as a new and rapidly growing research domain, allowed the retrieval of more quantitative data from standard medical images [37]. Studies on radiomics for predicting HNC response to induction chemotherapy and its prognosis have yielded good and groundbreaking results.…”

Section: Discussionmentioning

confidence: 99%

Pretreatment radiomics power in evaluating neoadjuvant chemotherapy response and outcome for patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

XinYao

et al. 2023

Preprint

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Background: This study conducted a meta-analysis of the evaluation value of radiomics in predicting induction chemotherapy (IC) response and prognosis of the patients with head and neck squamous cell carcinoma (HNSCC). By using the method of radiomic to formulate individualized treatment plans for the patients with HNSCC, the survival rate of the patients could be improved while maintaining the normal physiological functions. Methods: We systematically searched the relevant articles on radiomics predicting IC response and prognosis of the patients with HNSCC published before June 1, 2022. Meta-analysis was used to evaluate the power for radiomics in predicting the response and prognosis of patients with HNSCC to IC. Results: A total of 16 articles with 8238 patients were included in the study. The predictive value of IC response in patients with HNSCC showed that the diagnostic AUC was 0.87 (0.83-0.89), the sensitivity was 0.85 (0.69-0.94), and the specificity was 0.77 (0.65-0.86). By using radiomics, patients were divided into high-risk group and low-risk group, IC was the protective factor in the high-risk group. The survival time of overall survival (OS) in IC group was 0.35 (95% CI: 0.25-0.49) compared with the non-IC group, the disease progression free survival time (PFS) was 0.38 (95% CI: 0.28-0.52), and the metastasis free survival time (DMFS) was 0.37 (95% CI: 0.26-0.53). Conclusions: Radiomics has a good predictive ability for the response of patients with HNSCC to IC and the impact of prognosis. Radiomics will assist clinicians in making more individualized treatment decisions for patients with HNSCC.

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Section: Discussionmentioning

confidence: 99%

Pretreatment radiomics power in evaluating neoadjuvant chemotherapy response and outcome for patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

XinYao

et al. 2023

Preprint

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“…Radiomics has excellent prospects in multiple applications and can potentially aid in retrieving more quantitative data from standard medical images (47). In recent years, radiomics has developed rapidly in NPC research.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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PurposeThis 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.MethodsOur 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.ResultsA 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).ConclusionPrediction 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.

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“…Radiomics hold great promise as a modern approach to thyroid tumor diagnosis. 13 However, due to the limited code capabilities of our research group, we were unable to develop a feature extraction tool comparable to Pyradiomics. The reliability and stability of Pyradiomics have been widely recognized.…”

Section: Methodsmentioning

confidence: 99%

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

Ling

2022

Clin Med Insights Oncol

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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.

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Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Cited by 14 publications

References 108 publications

Pretreatment radiomics power in evaluating neoadjuvant chemotherapy response and outcome for patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

Pretreatment radiomics power in evaluating neoadjuvant chemotherapy response and outcome for patients with head and neck squamous cell carcinoma: a systematic review and meta-analysis

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

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

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