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Nayak, Umanath K.; Prasad, Ravikanti Satya; Sekhar, Shobana

doi:10.1007/978-981-10-5036-7_6

Cited by 1 publication

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“…Firstly, CT is more widely available across centers, requires reduced scan time, and entails lower expense than MRI (Junn et al 2021 ), thereby facilitating efficient high-volume analysis. In addition, CT provides higher resolution depiction of bone and vascular invasion, (Rumboldt et al 2006 ; Nayak et al 2018 ) which can be crucial for assessing tumor infiltration and treatment effects. Moreover, CT might be the only option for patients with MRI contraindications (e.g., intracranial metallic foreign bodies, claustrophobia) (Hiyama et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

CT-based peritumoral radiomics nomogram on prediction of response and survival to induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

Zeng,

Ye,

Zhou

2024

J Cancer Res Clin Oncol

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Purpose The study aims to harness the value of radiomics models combining intratumoral and peritumoral features obtained from pretreatment CT to predict treatment response as well as the survival of LA-NPC(locoregionally advanced nasopharyngeal carcinoma) patients receiving multiple types of induction chemotherapies, including immunotherapy and targeted therapy. Methods 276 LA-NPC patients (221 in the training and 55 in the testing cohort) were retrospectively enrolled. Various statistical analyses and feature selection techniques were applied to identify the most relevant radiomics features. Multiple machine learning models were trained and compared to build signatures for the intratumoral and each peritumoral region, along with a clinical signature. The performance of each model was evaluated using different metrics. Subsequently, a nomogram model was constructed by combining the best-performing radiomics and clinical models. Results In the testing cohort, the nomogram model exhibited an AUC of 0.816, outperforming the other models. The nomogram model’s calibration curve showed good agreement between predicted and observed outcomes in both the training and testing sets. When predicting survival, the model’s concordance index (C-index) was 0.888 in the training cohort and 0.899 in the testing cohort, indicating its robust predictive ability. Conclusion In conclusion, the combined nomogram model, incorporating radiomics and clinical features, outperformed other models in predicting treatment response and survival outcomes for LA-NPC patients receiving induction chemotherapies. These findings highlight the potential clinical utility of the model, suggesting its value in individualized treatment planning and decision-making.

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Section: Introductionmentioning

confidence: 99%