Additional investigations.

<p>Additional investigations.</p

Example head/neck area.

<p>Significant false-positive finding on PET/CT in the head/neck area. Intense FDG uptake in a lymph node (red arrow), revealing inflammation in a patient who eventually did not have lung cancer. Bowel-hotspots were ignored (blue arrows). Left: maximum intensity projection of PET. Right: Corresponding transverse slices of PET (top), PET/CT (middle), and CT (bottom).</p

FDG PET-findings summarised by scan area.

<p>FDG PET-findings summarised by scan area.</p

Example abdominal/pelvic area.

<p>True-positive but no-impact findings on PET/CT in the lower abdominal and pelvic area. Multiple skeletal metastases were detected (blue arrows), but these had no impact on staging as the thoracic field of view already showed multiple distant metastases (skeletal, adrenal glands, liver). An intense bowel hotspot (red arrow) is probably a colon carcinoma or large dysplastic polyp, but this was ignored as the prognosis of this patient was determined by the lung cancer. Imaging of the lower abdomen and pelvic area did not change the stage or therapy for this patient. Left: Coronal maximum intensity projection of PET. Right: Corresponding transverse slices of PET (top), PET/CT (middle), and CT (bottom).</p

Scan regions.

<p>The regions evaluated in this study. The head and neck area (HN): all above the shoulder line. The lower abdominal and pelvic area (LAP): all below the caudal tip of the right liver lobe. The region between HN and LAP (chest and upper abdomen, thoracic range) is the region of interest for imaging of (suspected) lung cancer.</p

Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer

<p><b>Background:</b> Radiomic features retrieved from standard CT-images have shown prognostic power in several tumor sites. In this study, we investigated the prognostic value of pretreatment CT radiomic features to predict overall survival of esophageal cancer patients after chemoradiotherapy.</p> <p><b>Material and methods:</b> Two datasets of independent centers were analyzed, consisting of esophageal cancer patients treated with concurrent chemotherapy (Carboplatin/Paclitaxel) and 41.4Gy radiotherapy, followed by surgery if feasible. In total, 1049 radiomic features were calculated from the primary tumor volume. Recursive feature elimination was performed to select the 40 most relevant predictors. Using these 40 features and six clinical variables as input, two random forest (RF) models predicting 3-year overall survival were developed.</p> <p><b>Results:</b> In total 165 patients from center 1 and 74 patients from center 2 were used. The radiomics-based RF model yielded an area under the curve (AUC) of 0.69 (95%CI 0.61–0.77), with the top-5 most important features for 3-year survival describing tumor heterogeneity after wavelet filtering. In the validation dataset, the RF model yielded an AUC of 0.61 (95%CI 0.47–0.75). Kaplan Meier plots were significantly different between risk groups in the training dataset (<i>p</i> = .027) and borderline significant in the validation dataset (<i>p</i> = .053). The clinical RF model yielded AUCs of 0.63 (95%CI 0.54–0.71) and 0.62 (95%CI 0.49–0.76) in the training and validation dataset, respectively. Risk groups did not reach a significant correlation with pathological response in the primary tumor.</p> <p><b>Conclusions:</b> A RF model predicting 3-year overall survival based on pretreatment CT radiomic features was developed and validated in two independent datasets of esophageal cancer patients. The radiomics model had better prognostic power compared to the model using standard clinical variables.</p

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study

<p><b>Background:</b> Radiomic analyses of CT images provide prognostic information that can potentially be used for personalized treatment. However, heterogeneity of acquisition- and reconstruction protocols influences robustness of radiomic analyses. The aim of this study was to investigate the influence of different CT-scanners, slice thicknesses, exposures and gray-level discretization on radiomic feature values and their stability.</p> <p><b>Material and methods:</b> A texture phantom with ten different inserts was scanned on nine different CT-scanners with varying tube currents. Scans were reconstructed with 1.5 mm or 3 mm slice thickness. Image pre-processing comprised gray-level discretization in ten different bin widths ranging from 5 to 50 HU and different resampling methods (i.e., linear, cubic and nearest neighbor interpolation to 1 × 1 × 3 mm³ voxels) were investigated. Subsequently, 114 textural radiomic features were extracted from a 2.1 cm³ sphere in the center of each insert. The influence of slice thickness, exposure and bin width on feature values was investigated. Feature stability was assessed by calculating the concordance correlation coefficient (CCC) in a test-retest setting and for different combinations of scanners, tube currents and slice thicknesses.</p> <p><b>Results:</b> Bin width influenced feature values, but this only had a marginal effect on the total number of stable features (CCC > 0.85) when comparing different scanners, slice thicknesses or exposures. Most radiomic features were affected by slice thickness, but this effect could be reduced by resampling the CT-images before feature extraction. Statistics feature ‘energy’ was the most dependent on slice thickness. No clear correlation between feature values and exposures was observed.</p> <p><b>Conclusions:</b> CT-scanner, slice thickness and bin width affected radiomic feature values, whereas no effect of exposure was observed. Optimization of gray-level discretization to potentially improve prognostic value can be performed without compromising feature stability. Resampling images prior to feature extraction decreases the variability of radiomic features.</p