Medical image processing and analysis (also known as Radiomics) is a rapidly growing discipline that maps digital medical images into quantitative data, with the end goal of generating imaging biomarkers as decision support tools for clinical practice. The use of imaging data from routine clinical work-up has tremendous potential in improving cancer care by heightening understanding of tumor biology and aiding in the implementation of precision medicine. As a noninvasive method of assessing the tumor and its microenvironment in their entirety, radiomics allows the evaluation and monitoring of tumor characteristics such as temporal and spatial heterogeneity. One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology. Nevertheless, the use of radiomics as clinical biomarkers still necessitates amelioration and standardization in order to achieve routine clinical adoption. This Review addresses the critical issues to ensure the proper development of radiomics as a biomarker and facilitate its implementation in clinical practice.

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Radiomics extracts high-throughput quantitative data from medical images to contribute to precision medicine. Radiomic shape features have been shown to correlate with patient outcomes. However, how radiomic shape features vary in function of tumor complexity and tumor volume, as well as with method used for meshing and voxel resampling, remains unknown. The aims of this study are to create tumor models with varying degrees of complexity, or spiculatedness, and evaluate their relationship with quantitatively extracted shape features. Twenty-eight tumor models were mathematically created using spherical harmonics with the spiculatedness degree d being increased by increments of 3 (d = 11 to d = 92). Models were 3D printed with identical bases of 5 cm, imaged with a CT scanner with two different slice thicknesses, and semi-automatically delineated. Resampling of the resulting masks on a 1 × 1 × 1 mm3 grid was performed, and the voxel size of each model was then calculated to eliminate volume differences. Four MATLAB-based algorithms (isosurface (M1), isosurface filter (M2), isosurface remeshing (M3), and boundary (M4)) were used to extract nine 3D features (Volume, Surface area, Surface-to-volume, Compactness1, Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity). To quantify the impact of 3D printing, acquisition, segmentation and meshing, features were computed directly from the stereolithography (STL) file format that was used for 3D printing, and compared to those computed. Changes in feature values between 0.6 and 2 mm slice acquisitions were also compared. Spearman’s rank-order correlation coefficients were computed to determine the relationship of each shape feature with spiculatedness for each of the four meshing algorithms. Percent changes were calculated between shape features extracted from the original and resampled contoured images to evaluate the influence of spatial resampling. Finally, the percent change in shape features when the volume was changed from 25% to 150% of their original volume was quantified for three distinct tumor models and compared to the percent change observed when modifying the spiculatedness of the model from d = 11 to d = 92. Values extracted using isosurface remeshing method are the closest to the STL reference ones, with mean differences less than 10.8% (Compactness2) for all features. Seven of the eight features had strong significant correlations with tumor model complexity irrespective of the meshing algorithm (r > 0.98, p < 10-4), with fractional concavity having the lowest correlation coefficient (r = 0.83, p < 10-4, M2). Comparisons of features extracted from the 0.6 and 2 mm slice thicknesses showed that mean differences were from 2.1% (Compactness3) to 12.7% (Compactness2) for the isosurface remeshing method. Resampling on a 1 × 1 × 1 mm3 grid resulted in between 1.3% (Compactness3) to 9.5% (Fractional Concavity) mean changes in feature values. Compactness2, Compactness3, Spherical Disproportion, Sphericity and Fractional Concavity...

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Purpose To evaluate the association between pelvic bone marrow (BM) dose volume parameters and probability of acute hematological toxicity (HT), a cohort of cervical cancer patients receiving definitive chemoradiation (CRT) was assessed. Materials and methods Medical records of patients treated by CRT (45 Gy in 25 fractions, without dose constraints applied to the BM) were reviewed. Baseline and weekly hematological parameters were collected. BM was retrospectively delineated and divided into sub-sites: iliac crests, lower pelvis, lumbosacral region. BM volumes (V) receiving 5, 10, 20, 30, 40 Gy (V5, V10, V20, V30, V40, respectively) and mean dose (Dm) were calculated. Logistic regression was used to analyze associations between HT and dose-volume histograms parameters. Results 114 patients were included. 75.4% were treated with 3D radiation therapy and 24.6% were receiving intensity modulated radiation therapy (IMRT). Neither age, chemotherapy regimen (cisplatin vs carboplatin), number of chemotherapy cycles, performance status, body mass index, or para-aortic irradiation were associated with HT. In univariate analysis, more frequent grade 3+ leukopenia was found in the IMRT group (odds ratio [OR]: 3.5; 95% CI, 1.4–9.1; p =0.007). In multivariate analysis, grade 4 HT was associated with lower pelvis V5>95% (OR 4.1; 95% CI, 1.6–14. p =0.02), lower pelvis V20>45% (OR 3.5; 95% CI, 1.1–13.4; p =0.05), total pelvic bone V20>65%, and iliac crests Dm >31 Gy (OR 4.5; 95% CI, 1.4–14.7; p =0.02). Conclusion The following dose constraints could be proposed to decrease acute HT risk: lower pelvis V5<95%, lower pelvis V20≤45%, total pelvic bone V20<65%, and iliac crests Dm <31 Gy.

This supports the need for prospective neutrophils evaluation in clinical trials, making neutrophils a predictive biomarker with potential specific therapies.

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