Perfusion CT radiomics as potential prognostic biomarker in head and neck squamous cell carcinoma

Bogowicz, Marta; Tanadini‐Lang, Stephanie; Veit-Haibach, Patrick; Pruschy, Martin; Bender, Sabine; Sharma, Ashish; Hüllner, Martin; Studer, Gabriela; Stieb, Sonja; Hemmatazad, Hossein; Glatz, S.; Gückenberger, Matthias

doi:10.1080/0284186x.2019.1629013

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…30 The underlying hypothesis is that this heterogeneity on imaging reflects the heterogeneity of the microenvironment (vascularization, necrosis, tumor hypoxia), which is known to be associated with a more aggressive tumor phenotype in head neck cancers. 31 For the same reason, CE-CT might be more informative for radiomics than noninjected CT. 32 Four different discretization settings of voxel intensities were implemented for each textural feature. The selected feature, Zone Percentage, was predictive when calculated using the fixed bin size method with 25 HU.…”

Section: Discussionmentioning

confidence: 99%

“…Second, manual segmentation by a single radiation oncologist (IM) was performed, which is time‐consuming and is a source of interoperator variability 37 . However, manual segmentation is mainly used in head and neck cancers 32,35,38–40 as these tumors are less suitable for automatic or semi‐automatic segmentation than other tumor locations. Moreover, the delineation of the GTV often requires the expertise of a radiation oncologist, as mainly based on clinical evaluation.…”

Section: Discussionmentioning

confidence: 99%

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Statistical harmonization can improve the development of a multicenter CT‐based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers

et al. 2021

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To develop a radiomic model predicting nonresponse to induction chemotherapy in laryngeal cancers, from multicenter pretherapeutic contrast-enhanced computed tomography (CE-CT) and evaluate the benefit of feature harmonization in such a context. Methods: Patients (n = 104) eligible for laryngeal preservation chemotherapy were included in five centers. Primary tumor was manually delineated on the CE-CT images. The following radiomic features were extracted with an in-house software (MIRAS v1.1, LaTIM UMR 1101): intensity, shape, and textural features derived from Gray-Level Co-occurrence Matrix (GLCM), Neighborhood Gray Tone Difference Matrix (NGTDM), Gray-Level Run Length Matrix (GLRLM), and Gray-Level Size Zone Matrix (GLSZM). Harmonization was performed using ComBat after unsupervised hierarchical clustering, used to determine labels automatically, given the high heterogeneity of imaging characteristics across and within centers. Patients with similar feature distributions were grouped with unsupervised clustering into an optimal number of clusters (2) determined with "silhouette scoring." Statistical harmonization was then carried out with ComBat on these 2 identified clusters. The cohort was split into training/validation (n = 66) and testing (n = 32) sets. Area under the receiver operating characteristics curves (AUC) were used to evaluate the ability of radiomic features (before and after harmonization) to predict nonresponse to chemotherapy, and specificity (Sp) and sensitivity (Se) were used to quantify their performance in the testing set. Results: Without harmonization, none of the features identified as predictive in the training set remained significant in the testing set. After ComBat, one textural feature identified in the training set keeps a predictive trend in the testing set-Zone Percentage, derived from the GLSZM, was predictive of nonresponse in the training set (AUC = 0.62, Se = 70%, Sp = 64%, P = 0.04) and obtained a satisfactory performance in the testing set (Se = 80%, Sp = 67%, P = 0.03), although significance was limited by the size of the testing set. These results are consistent with previously published findings in head and neck cancers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Statistical harmonization can improve the development of a multicenter CT‐based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers

et al. 2021

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“…Contrast-enhanced and non-contrast CT, (contrast-enhanced) T1 and T2 MRI sequences and FDG-PET imaging were all applied for radiomics based outcome prediction (Table 2) as well as some less common imaging techniques including diffusion-weighted MRI [28], 18F-fluorothymidine-PET [92], and perfusion CT [80]. Studies listed in Table 2 applied different analytical strategies, such as using single feature, feature combinations ("signatures", "scores") or more complex combined models; such analytical heterogeneity limits direct comparison of studies [97], and cannot be fully reflected in Table 2.…”

Section: Prediction Of Recurrence Treatment Response and Survival Imentioning

confidence: 99%

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas

et al. 2020

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Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the "-omics" concept for the broader field of head and neck cancer-"Radiomics". This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.

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“…Radiomics can effectively combine all CTP parameters with spatial information to guide treatment of patients with PDAC [63]. These datadriven biomarkers and their potential to improve tumor characterization and treatment assessment are increasingly investigated [63][64][65]. A combination of CTP and radiomic features already shows to improve the prediction of response in laryngeal cancer [66].…”

Section: Future Perspectivesmentioning

confidence: 99%

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

et al. 2021

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Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for ‘CTP’ and ‘PDAC.’ Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters. Graphic abstract

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Perfusion CT radiomics as potential prognostic biomarker in head and neck squamous cell carcinoma

Cited by 13 publications

References 33 publications

Statistical harmonization can improve the development of a multicenter CT‐based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers

Statistical harmonization can improve the development of a multicenter CT‐based radiomic model predictive of nonresponse to induction chemotherapy in laryngeal cancers

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

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