Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer.

BACKGROUND: To report on the discriminative ability of a simulation Computed Tomography (CT)-based radiomics signature for predicting response to treatment in patients undergoing neoadjuvant chemo-radiation for locally advanced adenocarcinoma of the rectum. METHODS: Consecutive patients treated at the Universities of Tübingen (from 1/1/07 to 31/12/10, explorative cohort) and Florence (from 1/1/11 to 31/12/17, external validation cohort) were considered in our dual-institution, retrospective analysis. Long-course neoadjuvant chemo-radiation was performed according to local policy. On simulation CT, the rectal Gross Tumor Volume was manually segmented. A feature selection process was performed yielding mineable data through an in-house developed software (written in Python 3.6). Model selection and hyper-parametrization of the model was performed using a fivefold cross validation approach. The main outcome measure of the study was the rate of pathologic good response, defined as the sum of Tumor regression grade (TRG) 3 and 4 according to Dworak's classification. RESULTS: Two-hundred and one patients were included in our analysis, of whom 126 (62.7%) and 75 (37.3%) cases represented the explorative and external validation cohorts, respectively. Patient characteristics were well balanced between the two groups. A similar rate of good response to neoadjuvant treatment was obtained in in both cohorts (46% and 54.7%, respectively; p = 0.247). A total of 1150 features were extracted from the planning scans. A 5-metafeature complex consisting of Principal component analysis (PCA)-clusters (whose main components are LHL Grey-Level-Size-Zone: Large Zone Emphasis, Elongation, HHH Intensity Histogram Mean, HLL Run-Length: Run Level Variance and HHH Co-occurence: Cluster Tendency) in combination with 5-nearest neighbour model was the most robust signature. When applied to the explorative cohort, the prediction of good response corresponded to an average Area under the curve (AUC) value of 0.65 ± 0.02. When the model was tested on the external validation cohort, it ensured a similar accuracy, with a slightly lower predictive ability (AUC of 0.63). CONCLUSIONS: Radiomics-based, data-mining from simulation CT scans was shown to be feasible and reproducible in two independent cohorts, yielding fair accuracy in the prediction of response to neoadjuvant chemo-radiation.

Comparison of patient stratification by computed tomography radiomics and hypoxia positron emission tomography in head-and-neck cancer radiotherapy.

BACKGROUND AND PURPOSE: Hypoxia Positron-Emission-Tomography (PET) as well as Computed Tomography (CT) radiomics have been shown to be prognostic for radiotherapy outcome. Here, we investigate the stratification potential of CT-radiomics in head and neck cancer (HNC) patients and test if CT-radiomics is a surrogate predictor for hypoxia as identified by PET. MATERIALS AND METHODS: Two independent cohorts of HNC patients were used for model development and validation, HN1 (n = 149) and HN2 (n = 47). The training set HN1 consisted of native planning CT data whereas for the validation cohort HN2 also hypoxia PET/CT data was acquired using [18F]-Fluoromisonidazole (FMISO). Machine learning algorithms including feature engineering and classifier selection were trained for two-year loco-regional control (LRC) to create optimal CT-radiomics signatures.Secondly, a pre-defined [18F]FMISO-PET tumour-to-muscle-ratio (TMRpeak ≥ 1.6) was used for LRC prediction. Comparison between risk groups identified by CT-radiomics or [18F]FMISO-PET was performed using area-under-the-curve (AUC) and Kaplan-Meier analysis including log-rank test. RESULTS: The best performing CT-radiomics signature included two features with nearest-neighbour classification (AUC = 0.76 ± 0.09), whereas AUC was 0.59 for external validation. In contrast, [18F]FMISO TMRpeak reached an AUC of 0.66 in HN2. Kaplan-Meier analysis of the independent validation cohort HN2 did not confirm the prognostic value of CT-radiomics (p = 0.18), whereas for [18F]FMISO-PET significant differences were observed (p = 0.02). CONCLUSIONS: No direct correlation of patient stratification using [18F]FMISO-PET or CT-radiomics was found in this study. Risk groups identified by CT-radiomics or hypoxia PET showed only poor overlap. Direct assessment of tumour hypoxia using PET seems to be more powerful to stratify HNC patients.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Radiogenomics in head and neck cancer: correlation of radiomic heterogeneity and somatic mutations in TP53, FAT1 and KMT2D.

PURPOSE: Genetic tumour profiles and radiomic features can be used to complement clinical information in head and neck squamous cell carcinoma (HNSCC) patients. Radiogenomics imply the potential to investigate complementarity or interrelations of radiomic and genomic features, and prognostic factors might be determined. The aim of our study was to explore radiogenomics in HNSCC. METHODS: For 20 HNSCC patients treated with primary radiochemotherapy, next-generation sequencing (NGS) of tumour and corresponding normal tissue was performed. In total, 327 genes were investigated by panel sequencing. Radiomic features were extracted from computed tomography data. A hypothesis-driven approach was used for radiogenomic correlations of selected image-based heterogeneity features and well-known driver gene mutations in HNSCC. RESULTS: The most frequently mutated driver genes in our cohort were TP53 (involved in cell cycle control), FAT1 (Wnt signalling, cell-cell contacts, migration) and KMT2D (chromatin modification). Radiomic features of heterogeneity did not correlate significantly with somatic mutations in TP53 or KMT2D. However, somatic mutations in FAT1 and smaller primary tumour volumes were associated with reduced radiomic intra-tumour heterogeneity. CONCLUSION: The landscape of somatic variants in our cohort is well in line with previous reports. An association of somatic mutations in FAT1 with reduced radiomic tumour heterogeneity could potentially elucidate the previously described favourable outcomes of these patients. Larger studies are needed to validate this exploratory data in the future.