Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score

Sanduleanu, Sebastian; Woodruff, Henry C.; Jong, Evelyn E.C. de; Timmeren, Janita E. van; Jochems, Arthur; Dubois, Ludwig

doi:10.1016/j.radonc.2018.03.033

Cited by 196 publications

(165 citation statements)

References 57 publications

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“…Moreover, a previous study reported that IA tends to appear more heterogeneous on CT images than PM [30]. Therefore, we hypothesize that radiomics features describing density and heterogeneity are related to tumor biology and pathology and are an excellent predictor for identification of IA [25]. CT and positron emission tomography radiomics studies have shown predictive features could be a surrogate of lesion volume and knowledge of which features correlate highly with volume is therefore important [31][32][33].…”

Section: Discussionmentioning

confidence: 92%

“…This study was followed by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [24]. The radiomics quality score (RQS) was used to evaluate the radiomics workflow [25]. Pearson's chi-squared test was used for the statistical analysis of essential demographic characteristics.…”

Section: Tripod Guidelines Radiomics Quality Score and Statistical mentioning

confidence: 99%

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Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Woodruff

Sanduleanu

et al. 2020

Eur Radiol

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Objectives Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). Methods This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. ResultsThe radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. Conclusions Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. Key Points • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

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Section: Discussionmentioning

confidence: 92%

Section: Tripod Guidelines Radiomics Quality Score and Statistical mentioning

confidence: 99%

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Woodruff

Sanduleanu

et al. 2020

Eur Radiol

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“…In principle, connected biomarkers open new avenues to therapeutic paths, and allows assessment of emerging digital biomarkers (12). Here, we demonstrate the applicability of a PM network approach in onco-radiomics (13,14). We considered patients affected by intracranial ependymoma whose response to radiotherapy was assessed by MRI.…”

Section: Our Contributionmentioning

confidence: 99%

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

et al. 2020

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Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

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“…Nevertheless, one of the main reasons for the still limited impact of radiomics on everyday practice may be the difficulty in interpreting the relevant imaging biomarkers (such as the radiomic features) in biological terms. 30 Future studies will need to investigate how tumour biology and phenotypes translate into imaging features. Furthermore, most of the results need to be validated on large prospective independent cohorts.…”

Section: Radiomics For Cancer Diagnosismentioning

confidence: 99%

Radiomics for radiation oncologists: are we ready to go?

et al. 2020

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Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.

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Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score

Cited by 196 publications

References 57 publications

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study

Role of Complex Networks for Integrating Medical Images and Radiomic Features of Intracranial Ependymoma Patients in Response to Proton Radiotherapy

Radiomics for radiation oncologists: are we ready to go?

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