Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans

Buizza, Giulia; Toma-Daşu, Iuliana; Lazzeroni, M.; Paganelli, Chiara; Riboldi, Marco; Chang, Yong Jun; Smedby, Örjan

doi:10.1016/j.ejmp.2018.09.003

Cited by 40 publications

(21 citation statements)

References 30 publications

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“…However, radiomic features can provide more comprehensive information about tumor makeup. A study showed that a novel set of quantitative image features, based on heterogeneities of tumor physiology, was helpful for early prediction of treatment outcome 31 . Therefore, we obtained AUC2 values for 23 quantitative features, including volume, that were previously reported to be associated with lung cancer 32–37 .…”

Section: Discussionmentioning

confidence: 99%

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Bak

Park

Sohn

et al. 2019

Sci Rep

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Tumor growth dynamics vary substantially in non-small cell lung cancer (NSCLC). We aimed to develop biomarkers reflecting longitudinal change of radiomic features in NSCLC and evaluate their prognostic power. Fifty-three patients with advanced NSCLC were included. Three primary variables reflecting patterns of longitudinal change were extracted: area under the curve of longitudinal change (AUC1), beta value reflecting slope over time, and AUC2, a value obtained by considering the slope and area over the longitudinal change of features. We constructed models for predicting survival with multivariate cox regression, and identified the performance of these models. AUC2 exhibited an excellent correlation between patterns of longitudinal volume change and a significant difference in overall survival time. Multivariate regression analysis based on cut-off values of radiomic features extracted from baseline CT and AUC2 showed that kurtosis of positive pixel values and surface area from baseline CT, AUC2 of density, skewness of positive pixel values, and entropy at inner portion were associated with overall survival. For the prediction model, the areas under the receiver operating characteristic curve (AUROC) were 0.948 and 0.862 at 1 and 3 years of follow-up, respectively. Longitudinal change of radiomic tumor features may serve as prognostic biomarkers in patients with advanced NSCLC.

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Section: Discussionmentioning

confidence: 99%

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Bak

Park

Sohn

et al. 2019

Sci Rep

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“…Pattern recognition is essential in radiology; however, utilization of quantitative image analysis will provide objective additional data on tumor aggressiveness. These techniques have been applied in lung cancer and others [32–34]. A review on other techniques for small renal mass characterization will be utilized in further evaluation and improvements [35, 36].…”

Section: Discussionmentioning

confidence: 99%

Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes

Rajendran

Iffrig

Pruthi

et al. 2019

Advances in Urology

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Objective. To develop software to assess the potential aggressiveness of an incidentally detected renal mass using images. Methods. Thirty randomly selected patients who underwent nephrectomy for renal cell carcinoma (RCC) had their images independently reviewed by engineers. Tumor “Roughness” was based on image algorithm of tumor topographic features visualized on computed tomography (CT) scans. Univariant and multivariant statistical analyses are utilized for analysis. Results. We investigated 30 subjects that underwent partial or radical nephrectomy. After excluding poor image-rendered images, 27 patients remained (benign cyst = 1, oncocytoma = 2, clear cell RCC = 15, papillary RCC = 7, and chromophobe RCC = 2). The mean roughness score for each mass is 1.18, 1.16, 1.27, 1.52, and 1.56 units, respectively (p<0.004). Renal masses were correlated with tumor roughness (Pearson’s, p=0.02). However, tumor size itself was larger in benign tumors (p=0.1). Linear regression analysis noted that the roughness score is the most influential on the model with all other demographics being equal including tumor size (p=0.003). Conclusion. Using basic CT imaging software, tumor topography (“roughness”) can be quantified and correlated with histologies such as RCC subtype and could lead to determining aggressiveness of small renal masses.

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“…28 In addition, Buizza et al showed that the longitudinal temporal and spatial changes from PET/CT imaging could improve early survival prediction for chemoradiation treatment. 29 Radiomics has also been used to predict radiation-induced normal tissue toxicities, such as radiation pneumonitis 21 or xerostomia. 30,31 These results suggest that a radiomics-based signature may emerge as an accepted imaging biomarker for predicting therapeutic outcome and for improving decision support in cancer treatment.…”

Section: Tumor Detection and Diagnosismentioning

confidence: 99%

NCTN Assessment on Current Applications of Radiomics in Oncology

Nie

Al‐Hallaq

et al. 2019

International Journal of Radiation Oncology*Biology*Physics

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Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In NotedAn online CME test for this article can be taken at https:// academy.astro.org.

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Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans

Cited by 40 publications

References 30 publications

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Prognostic Impact of Longitudinal Monitoring of Radiomic Features in Patients with Advanced Non-Small Cell Lung Cancer

Initial Evaluation of Computer-Assisted Radiologic Assessment for Renal Mass Edge Detection as an Indication of Tumor Roughness to Predict Renal Cancer Subtypes

NCTN Assessment on Current Applications of Radiomics in Oncology

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