Oncologic treatment support via a dedicated mobile app: a prospective feasibility evaluation (OPTIMISE-1).

BACKGROUND: Mobile health (mhealth) is gaining interest, with mobile devices and apps being ever more available among medical facilities and patients. However, in the field of radiation oncology, the medical benefits of mhealth apps are still underexplored. As an additional approach to patient care during radiotherapy, we designed a mobile treatment surveillance app based on patient-reported outcomes. OBJECTIVE: We aimed to examine the feasibility of app-based treatment surveillance in patients undergoing radiotherapy (RT). Alongside technical practicability and acceptance, we assessed patient satisfaction and quality of life during treatment. METHODS: This prospective single-center study was performed at Heidelberg University Hospital between August 2018 and January 2020. During RT we measured patients' quality of life, symptoms, and treatment satisfaction. Respective questionnaires (EORTC QLQ-C30 with diagnosis-specific modules, RAND PSQ-18) were presented to patients via a mobile app running on a designated tablet device. The primary endpoint was determined by the fraction of patients who completed at least 80% of the items. Secondary endpoints were disease-related quality of life and patient satisfaction. RESULTS: A total of 49 cancer patients (14 breast, 13 pelvic, 12 lung, 10 prostate) were eligible for analysis. 79.6% (95% confidence interval: 66.4-88.5%; n = 39) of all patients completed at least 80% of the items received by the mobile app. A mean of 227.5 ± 48.25 questions were answered per patient. Breast cancer patients showed the highest rate of answered questions, with 92.9% (n = 13) completing at least 80% of the items. CONCLUSION: Patients showed high acceptance, with 79.6% (n = 39) completing at least 80% of the given items. The use of a mobile app for reporting symptoms and quality of life during RT is feasible and well accepted by patients. It may allow for resource-efficient, detailed feedback to the medical staff and assist in the assessment of side effects over time.

Preoperative Dose-Escalated Intensity-Modulated Radiotherapy (IMRT) and Intraoperative Radiation Therapy (IORT) in Patients with Retroperitoneal Soft-Tissue Sarcoma: Final Results of a Clinical Phase I/II Trial.

BACKGROUND: To report the final results of a prospective, one-armed, single-center phase I/II trial (NCT01566123). METHODS: Between 2007 and 2017, 37 patients with primary or recurrent (N = 6) retroperitoneal sarcomas were enrolled. Treatment included preoperative IMRT of 45-50 Gy with a simultaneous integrated boost of 50-56 Gy, surgery and IORT. The primary endpoint was local control (LC) at 5 years. The most common histology was dedifferentiated liposarcoma (51%), followed by leiomyosarcoma (24%) and well-differentiated liposarcoma (14%). The majority of lesions were high-grade (FNCLCC G1: 30%, G2: 38%, G3: 27%, two missing). Five patients were excluded from LC analysis per protocol. RESULTS: The minimum follow-up of the survivors was 62 months (median: 109; maximum 162). IORT was performed for 27 patients. Thirty-five patients underwent gross total resection; the pathological resection margin was mostly R+ (80%) and, less often, R0 (20%). We observed 10 local recurrences. The 5-year LC of the whole cohort was 59.6%. Eleven patients received a dose > 50 Gy plus IORT boost; LC was 64.8%; the difference, however, was not significant (p = 0.588). Of 37 patients, 15 were alive and 22 deceased at the time of final analysis. The 5-year OS was 59.5% (68.8% per protocol). CONCLUSIONS: The primary endpoint of a 5-year LC of 70% was not met. This might be explained by the inclusion of recurrent disease and the high rate of G3 lesions and leiomyosarcoma, which have been shown to profit less from radiotherapy. Stratification by grading and histology should be considered for future studies.

MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem.

BACKGROUND: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. METHODS: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes: T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20,101 MRI images) was used for training, while C2 (197, 11,333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics model performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). RESULTS: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95% Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. A total of 620 images were misclassified; manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased the PFS model C-I by 14.6% on average, compared to a model trained without MR-Class. CONCLUSIONS: We provide a DCNN-based method for the sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

MR Intensity Normalization Methods Impact Sequence Specific Radiomics Prognostic Model Performance in Primary and Recurrent High-Grade Glioma.

PURPOSE: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models' performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). METHODS: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods' performance. RESULTS: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman's rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. CONCLUSION: The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.

The impact of tumor metabolic activity assessed by 18F-FET amino acid PET imaging in particle radiotherapy of high-grade glioma patients.

Background: Selective uptake of (18)F-fluoro-ethyl-tyrosine (18F-FET) is used in high-grade glioma (HGG) to assess tumor metabolic activity via positron emission tomography (PET). We aim to investigate its value for target volume definition, as a prognosticator, and associations with whole-blood transcriptome liquid biopsy (WBT lbx) for which we recently reported feasibility to mirror tumor characteristics and response to particle irradiation in recurrent HGG (rHGG). Methods: 18F-FET-PET data from n = 43 patients with primary glioblastoma (pGBM) and n = 33 patients with rHGG were assessed. pGBM patients were irradiated with photons and sequential proton/carbon boost, and rHGG patients were treated with carbon re-irradiation (CIR). WBT (Illumina HumanHT-12 Expression BeadChips) lbx was available for n = 9 patients from the rHGG cohort. PET isocontours (40%-70% SUVmax, 10% steps) and MRI-based treatment volumes (MRIvol) were compared using the conformity index (CI) (pGBM, n = 16; rHGG, n = 27). Associations with WBT lbx data were tested on gene expression level and inferred pathways activity scores (PROGENy) and from transcriptome estimated cell fractions (CIBERSORT, xCell). Results: In pGBM, median SUVmax was higher in PET acquired pre-radiotherapy (4.1, range (R) 1.5-7.8; n = 20) vs. during radiotherapy (3.3, R 1.5-5.7, n = 23; p = 0.03) and in non-resected (4.7, R 2.9-7.9; n = 11) vs. resected tumors (3.3, R 1.5-7.8, n = 32; p = 0.01). In rHGG, a trend toward higher SUVmax values in grade IV tumors was observed (p = 0.13). Median MRIvol was 32.34 (R 8.75-108.77) cm3 in pGBM (n = 16) and 20.77 (R 0.63-128.44) cm3 in rHGG patients (n = 27). The highest median CI was observed for 40% (pGBM, 0.31) and 50% (rHGG, 0.43, all tumors) isodose, with 70% (40%) isodose in grade III (IV) rHGG tumors (median CI, 0.38 and 0.49). High SUVmax was linked to shorter survival in pGBM (>3.3, p = 0.001, OR 6.0 [2.1-17.4]) and rHGG (>2.8, p = 0.02, OR 4.1 [1.2-13.9]). SUVmax showed associations with inferred monocyte fractions, hypoxia, and TGFbeta pathway activity and links to immune checkpoint gene expression from WBT lbx. Conclusion: The benefits of 18F-FET-PET imaging on gross tumor volume (GTV) definition for particle radiotherapy warrant further evaluation. SUVmax might assist in prognostic stratification of HGG patients for particle radiotherapy, highlights heterogeneity in rHGG, and is positively associated with unfavorable signatures in peripheral whole-blood transcriptomes.

Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice.

Purpose: To develop a model to accurately segment mouse lungs with varying levels of fibrosis and investigate its applicability to mouse images with different resolutions. Materials and Methods: In this experimental retrospective study, a U-Net was trained to automatically segment lungs on mouse CT images. The model was trained (n = 1200), validated (n = 300), and tested (n = 154) on longitudinally acquired and semiautomatically segmented CT images, which included both healthy and irradiated mice (group A). A second independent group of 237 mice (group B) was used for external testing. The Dice score coefficient (DSC) and Hausdorff distance (HD) were used as metrics to quantify segmentation accuracy. Transfer learning was applied to adapt the model to high-spatial-resolution mouse micro-CT segmentation (n = 20; group C [n = 16 for training and n = 4 for testing]). Results: The trained model yielded a high median DSC in both test datasets: 0.984 (interquartile range [IQR], 0.977-0.988) in group A and 0.966 (IQR, 0.955-0.972) in group B. The median HD in both test datasets was 0.47 mm (IQR, 0-0.51 mm [group A]) and 0.31 mm (IQR, 0.30-0.32 mm [group B]). Spatially resolved quantification of differences toward reference masks revealed two hot spots close to the air-tissue interfaces, which are particularly prone to deviation. Finally, for the higher-resolution mouse CT images, the median DSC was 0.905 (IQR, 0.902-0.929) and the median 95th percentile of the HD was 0.33 mm (IQR, 2.61-2.78 mm). Conclusion: The developed deep learning-based method for mouse lung segmentation performed well independently of disease state (healthy, fibrotic, emphysematous lungs) and CT resolution.Keywords: Deep Learning, Lung Fibrosis, Radiation Therapy, Segmentation, Animal Studies, CT, Thorax, Lung Supplemental material is available for this article. Published under a CC BY 4.0 license.

The role of combined ion-beam radiotherapy (CIBRT) with protons and carbon ions in a multimodal treatment strategy of inoperable osteosarcoma.

BACKGROUND: To investigate the role of combined ion-beam radiotherapy (CIBRT) with protons and carbon ions in a multimodal treatment strategy of inoperable osteosarcoma; final analysis of a one-armed, single center phase I/II trial. METHODS: Between August 2011 until September 2018, 20 patients with primary (N = 18), metastatic (N = 3), or recurrent (N = 2) inoperable pelvic (70%) or craniofacial (30%) osteosarcoma were treated with protons up to 54 Gy (RBE) and a carbon ion boost of 18 Gy (RBE) and followed until May 2019. A Fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) was performed before CIBRT in search for a prognostic factor. The primary endpoint was toxicity. Secondary endpoints included treatment response, global, local and distant progression free survival (PFS, LPFS and DPFS) and overall (OS), among others. RESULTS: The median age was 20; all patients finished treatment per protocol. LPFS, DPFS, PFS and OS were 73%, 74%, 60% and 75% after one year and 55%, 65% 65.3%, 45% and 68% after two years, respectively. The median clinical target volume (CTV) was 1042 cc and 415 cc for the primary and boost plan, respectively. Craniofacial localization, lower uptake of FDG in PET/CT and boost plan CTV ≤ median were associated with improved overall survival (p = 0.039, p = 0.016 and p = 0.0043, respectively). No acute toxicities > grade III were observed. We observed one case of secondary acute myeloid leukemia (AML) seven months after CIBRT for recurrent disease and one case of hearing loss. CONCLUSION: CIBRT shows a favorable toxicity profile and promising results particularly for patients with inoperable craniofacial osteosarcoma.

Consolidation Immunotherapy After Platinum-Based Chemoradiotherapy in Patients With Unresectable Stage III Non-Small Cell Lung Cancer-Cross-Sectional Study of Eligibility and Administration Rates.

INTRODUCTION: The PACIFC trial demonstrated a significant benefit of durvalumab consolidation immunotherapy (CIT) after definitive platinum-based chemoradiotherapy (P-CRT) for survival in stage III non-small cell lung cancer (NSCLC). It is unknown how many patients are eligible in clinical practice to receive CIT according to PACIFIC criteria compared to real administration rates and what influencing factors are. PATIENTS AND METHODS: We analyzed 442 patients with unresectable stage III NSCLC who received P-CRT between 2009 and 2019 regarding CIT eligibility rates according to PACIFIC criteria and administration rates since drug approval. RESULTS: Sixty-four percent of 437 patients were male, median age was 63 years [interquartile range (IQR): 57-69]. The most common histologic subtypes were adenocarcinoma (42.8%) and squamous cell carcinoma (41.1%), most tumors were in stage IIIB (56.8%). Mean PD-L1 tumor proportion score (TPS) was 29.8% (IQR: 1-60). The median total RT dose was 60 Gy (IQR: 60-66). Platinum component of P-CRT was evenly distributed between cisplatin (51.4%) and carboplatin (48.6%). 50.3% of patients were eligible for CIT according to PACIFIC criteria. Observed contraindications were progressive disease according to RECIST (32.4%), followed by a PD-L1 TPS < 1% (22.3%), pneumonitis CTCAE ≥ 2 (12.6%) and others (4.9%). One year after drug approval, 85.6% of patients who were eligible according to PACIFIC criteria actually received CIT. Time interval between chemotherapy start and radiation therapy start (OR 0.9, 95% CI: [0.9; 1.0] p = 0.009) and probably cisplatin as platinum-component of P-CRT (OR 1.5, 95% CI: [1.0; 2.4] p < 0.061) influence CIT eligibility. Highly positive PD-L1 TPS (≥50%; (OR 2.4, 95% CI: [1.3; 4.5] p = 0.004) was associated to a better chance for CIT eligibility. CONCLUSION: Eighty-five percent of potentially eligible patients received CIT one year after drug approval. Fifty percent of patients did not meet PACIFIC criteria for durvalumab eligibility, this was mainly caused by disease progression during platinum-based CRT, followed by therapy-related pneumonitis and PD-L1 TPS < 1% (in view of the EMA drug approval).