Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Cai, Chunya; Lv, Wenbing; Chen, Feng; Zhang, Bailin; Zhu, Lin; Geng, Yun; Shi-wu, Zhao; Zhu, Yong; Xu, Han; Dai, Zhenhui; Wang, Xuetao; Lu, Lijun

doi:10.1002/mp.16044

Cited by 3 publications

(1 citation statement)

References 77 publications

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“…However, most previous research has focused on MRI-based radiomics, intra-tumoral regions, and a limited number of patients(Zhao et al 2020 ; Yongfeng et al 2021 ; Cai et al 2022 ). There is a lack of studies combining clinical data and imaging features, especially in patients receiving targeted therapy or immunotherapy.…”

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%

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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Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study

Mansouri,

Salimi,

Amini

et al. 2024

Radiat Oncol

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Background This study aimed to investigate the value of clinical, radiomic features extracted from gross tumor volumes (GTVs) delineated on CT images, dose distributions (Dosiomics), and fusion of CT and dose distributions to predict outcomes in head and neck cancer (HNC) patients. Methods A cohort of 240 HNC patients from five different centers was obtained from The Cancer Imaging Archive. Seven strategies, including four non-fusion (Clinical, CT, Dose, DualCT-Dose), and three fusion algorithms (latent low-rank representation referred (LLRR),Wavelet, weighted least square (WLS)) were applied. The fusion algorithms were used to fuse the pre-treatment CT images and 3-dimensional dose maps. Overall, 215 radiomics and Dosiomics features were extracted from the GTVs, alongside with seven clinical features incorporated. Five feature selection (FS) methods in combination with six machine learning (ML) models were implemented. The performance of the models was quantified using the concordance index (CI) in one-center-leave-out 5-fold cross-validation for overall survival (OS) prediction considering the time-to-event. Results The mean CI and Kaplan-Meier curves were used for further comparisons. The CoxBoost ML model using the Minimal Depth (MD) FS method and the glmnet model using the Variable hunting (VH) FS method showed the best performance with CI = 0.73 ± 0.15 for features extracted from LLRR fused images. In addition, both glmnet-Cindex and Coxph-Cindex classifiers achieved a CI of 0.72 ± 0.14 by employing the dose images (+ incorporated clinical features) only. Conclusion Our results demonstrated that clinical features, Dosiomics and fusion of dose and CT images by specific ML-FS models could predict the overall survival of HNC patients with acceptable accuracy. Besides, the performance of ML methods among the three different strategies was almost comparable.

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Dosiomics in the analysis of medical images and prospects for its use in clinical practice

Solodkiy,

Nudnov,

Ivannikov

et al. 2023

Digital Diagnostics

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BACKGROUND: In recent years, there has been a notable increase in the number of articles using the term dosiomics. However, there are no literature reviews on this topic in the Russian language. AIM: This study aims to describe the basic principles of dosiomics as a derivative of radiomics and to analyze studies devoted to assessing the possibilities of its application in clinical practice. MATERIALS AND METHODS: A systematic literature search was performed in the PubMed database using the search query dosiomics OR dosiomic, and in the eLibrary database using the search query dosiomics. By April 2023, 43 foreign articles and 1 Russian article had been published. RESULTS: The analysis encompassed 43 foreign studies investigating the use of dosiomics in clinical practice, alongside one Russian article that provided a definition of the term dosiomics. The analyzed papers were divided into three groups according to their subject matter, and two tables describing the results of 27 studies on the prediction of clinical outcomes were created. CONCLUSION: Currently, dosiomics is a new and promising derivative of radiomics used in the textural analysis of medical images associated with radiation treatment of cancer patients. Dosiomics can contribute to the development of a more personalized approach to the planning of radiotherapy, the prediction of radiation damage of normal tissues, and the diagnosis of recurrence.

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Prognostic generalization of multi‐level CT‐dose fusion dosiomics from primary tumor and lymph node in nasopharyngeal carcinoma

Cited by 3 publications

References 77 publications

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

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

Development and validation of survival prognostic models for head and neck cancer patients using machine learning and dosiomics and CT radiomics features: a multicentric study

Dosiomics in the analysis of medical images and prospects for its use in clinical practice

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