Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using MR imaging

BACKGROUND: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation

Prognostic assessment in high-grade soft-tissue sarcoma patients: A comparison of semantic image analysis and radiomics

BACKGROUND: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients\u27 risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). METHODS: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. RESULTS: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. CONCLUSIONS: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment

Remote symptom monitoring of patients with cancer undergoing radiation therapy

The goal of the study was to develop and test an automated short message service (SMS) and web service platform using CareSignal for remote symptom monitoring in a diverse population of patients with cancer. Twenty-eight patients with cancer undergoing radiotherapy were recruited at the start of their treatment regimen. Patients received a weekly SMS symptom survey to assess the severity of the side effects they experienced from treatment. An assessment of patient perceptions of the system in terms of patient-provider communication, feasibility, and overall satisfaction was conducted, finding overall good compliance in a sick patient population and patient willingness to engage with the software in the future

Treatment of stage I lung cancer detected by computed tomography screening

INTRODUCTION: Reducing lung cancer deaths through early detection by computed tomography (CT) screening requires delivery of effective treatment. We performed this retrospective study to determine the types of treatment used for screen-detected stage I lung cancer at our academic center and to compare the demographic and clinical characteristics of patients by type of treatment. METHODS: All persons screened in the lung cancer screening program at our institution through June 16, 2021, were included. Those with screening CT findings needing follow-up were managed through a thoracic surgery clinic. Demographic and clinical characteristics of patients diagnosed with having stage I lung cancer through June 16, 2021, were compared by type of treatment, with follow-up through December 31, 2021. RESULTS: Stage I NSCLC was diagnosed in 54 of 2203 persons screened (2.5%), on the basis of biopsy in 37 and on imaging findings in 17 patients in whom a tissue diagnosis could not be obtained. Treatment was by lobectomy in 18, sublobar resection in 14, and stereotactic body radiation therapy (SBRT) in 22. Patients treated with SBRT had lower forced expiratory volume in 1 second ( CONCLUSIONS: Many patients with screen-detected stage I lung cancer are medically unfit for lobectomy, and a variety of treatments are being used. Assessment of treatment-based outcomes will be critical for ensuring an optimal balance of the risks and benefits of CT screening in a medically diverse population

MRI radiomic features are independently associated with overall survival in soft tissue sarcoma

Purpose: Soft tissue sarcomas (STS) represent a heterogeneous group of diseases, and selection of individualized treatments remains a challenge. The goal of this study was to determine whether radiomic features extracted from magnetic resonance (MR) images are independently associated with overall survival (OS) in STS. Methods and Materials: This study analyzed 2 independent cohorts of adult patients with stage II-III STS treated at center 1 (N = 165) and center 2 (N = 61). Thirty radiomic features were extracted from pretreatment T1-weighted contrast-enhanced MR images. Prognostic models for OS were derived on the center 1 cohort and validated on the center 2 cohort. Clinical-only (C), radiomics-only (R), and clinical and radiomics (C+R) penalized Cox models were constructed. Model performance was assessed using Harrell\u27s concordance index. Results: In the R model, tumor volume (hazard ratio [HR], 1.5) and 4 texture features (HR, 1.1-1.5) were selected. In the C+R model, both age (HR, 1.4) and grade (HR, 1.7) were selected along with 5 radiomic features. The adjusted c-indices of the 3 models ranged from 0.68 (C) to 0.74 (C+R) in the derivation cohort and 0.68 (R) to 0.78 (C+R) in the validation cohort. The radiomic features were independently associated with OS in the validation cohort after accounting for age and grade (HR, 2.4; Conclusions: This study found that radiomic features extracted from MR images are independently associated with OS when accounting for age and tumor grade. The overall predictive performance of 3-year OS using a model based on clinical and radiomic features was replicated in an independent cohort. Optimal models using clinical and radiomic features could improve personalized selection of therapy in patients with STS

Simulation-free radiation therapy: An emerging form of treatment planning to expedite plan generation for patients receiving palliative radiation therapy

PURPOSE: Herein we report the clinical and dosimetric experience for patients with metastases treated with palliative simulation-free radiation therapy (SFRT) at a single institution. METHODS AND MATERIALS: SFRT was performed at a single institution. Multiple fractionation regimens were used. Diagnostic imaging was used for treatment planning. Patient characteristics as well as planning and treatment time points were collected. A matched cohort of patients with conventional computed tomography simulation radiation therapy (CTRT) was acquired to evaluate for differences in planning and treatment time. SFRT dosimetry was evaluated to determine the fidelity of SFRT. Descriptive statistics were calculated on all variables and statistical significance was evaluated using the Wilcoxon signed rank test and RESULTS: Thirty sessions of SFRT were performed and matched with 30 sessions of CTRT. Seventy percent of SFRT and 63% of CTRT treatments were single fraction. The median time to plan generation was 0.88 days (0.19-1.47) for SFRT and 1.90 days (0.39-5.23) for CTRT ( CONCLUSIONS: Palliative SFRT is an emerging technique that allowed for a statistically significant lower time to plan generation and was dosimetrically acceptable. This benefit must be weighed against increased total treatment time for patients receiving SFRT compared with CTRT, and appropriate patient selection is critical

Spatially fractionated stereotactic body radiation therapy (Lattice) for large tumors

Purpose: Stereotactic body radiation therapy (SBRT) has demonstrated clinical benefits for patients with metastatic and/or unresectable cancer. Technical considerations of treatment delivery and nearby organs at risk can limit the use of SBRT in large tumors or those in unfavorable locations. Spatially fractionated radiation therapy (SFRT) may address this limitation because this technique can deliver high-dose radiation to discrete subvolume vertices inside a tumor target while restricting the remainder of the target to a safer lower dose. Indeed, SFRT, such as GRID, has been used to treat large tumors with reported dramatic tumor response and minimal side effects. Lattice is a modern approach to SFRT delivered with arc-based therapy, which may allow for safe, high-quality SBRT for large and/or deep tumors. Methods and Materials: Herein, we report the results of a dosimetry and quality assurance feasibility study of Lattice SBRT in 11 patients with 12 tumor targets, each ≥10 cm in an axial dimension. Prior computed tomography simulation scans were used to generate volumetric modulated arc therapy Lattice SBRT plans that were then delivered on clinically available Linacs. Quality assurance testing included external portal imaging device and ion chamber analyses. Results: All generated plans met the standard SBRT dose constraints, such as those from the American Association of Physicists in Medicine Task Group 101. Additionally, we provide a step-by-step approach to generate and deliver Lattice SBRT plans using commercially available treatment technology. Conclusions: Lattice SBRT is currently being tested in a prospective trial for patients with metastatic cancer who need palliation of large tumors (NCT04553471, NCT04133415)

Implementing a novel remote physician treatment coverage practice for adaptive radiation therapy during the coronavirus pandemic

Purpose: The 2019 coronavirus disease pandemic has placed an increased importance on physical distancing to minimize the risk of transmission in radiation oncology departments. The pandemic has also increased the use of hypofractionated treatment schedules where magnetic resonance-guided online adaptive radiation therapy (ART) can aid in dose escalation. This specialized technique requires increased staffing in close proximity, and thus the need for novel coverage practices to increase physical distancing while still providing specialty care. Methods and Materials: A remote-physician ART coverage practice was developed and described using commercially available software products. Our remote-physician coverage practice provided control to the physician to contour and review of the images and plans. The time from completion of image registration to the beginning of treatment was recorded for 20 fractions before remote-physician ART coverage and 14 fractions after implementation of remote-physician ART coverage. Visual quality was calculated using cross-correlation between the treatment delivery and remote-physician computer screens. Results: For the 14 fractions after implementation, the average time from image registration to the beginning of treatment was 24.9 ± 6.1 minutes. In comparison, the 20 fractions analyzed without remote coverage had an average time of 29.2 ± 9.8 minutes. The correlation between the console and remote-physician screens was Conclusions: Our novel remote-physician ART coverage practice is secure, interactive, timely, and of high visual quality. When using remote physicians for ART, our department was able to increase physical distancing to lower the risk of virus transmission while providing specialty care to patients in need

Cell-free DNA ultra-low-pass whole genome sequencing to distinguish malignant peripheral nerve sheath tumor (MPNST) from its benign precursor lesion: A cross-sectional study

BACKGROUND: The leading cause of mortality for patients with the neurofibromatosis type 1 (NF1) cancer predisposition syndrome is the development of malignant peripheral nerve sheath tumor (MPNST), an aggressive soft tissue sarcoma. In the setting of NF1, this cancer type frequently arises from within its common and benign precursor, plexiform neurofibroma (PN). Transformation from PN to MPNST is challenging to diagnose due to difficulties in distinguishing cross-sectional imaging results and intralesional heterogeneity resulting in biopsy sampling errors. METHODS AND FINDINGS: This multi-institutional study from the National Cancer Institute and Washington University in St. Louis used fragment size analysis and ultra-low-pass whole genome sequencing (ULP-WGS) of plasma cell-free DNA (cfDNA) to distinguish between MPNST and PN in patients with NF1. Following in silico enrichment for short cfDNA fragments and copy number analysis to estimate the fraction of plasma cfDNA originating from tumor (tumor fraction), we developed a noninvasive classifier that differentiates MPNST from PN with 86% pretreatment accuracy (91% specificity, 75% sensitivity) and 89% accuracy on serial analysis (91% specificity, 83% sensitivity). Healthy controls without NF1 (participants = 16, plasma samples = 16), PN (participants = 23, plasma samples = 23), and MPNST (participants = 14, plasma samples = 46) cohorts showed significant differences in tumor fraction in plasma (P = 0.001) as well as cfDNA fragment length (P \u3c 0.001) with MPNST samples harboring shorter fragments and being enriched for tumor-derived cfDNA relative to PN and healthy controls. No other covariates were significant on multivariate logistic regression. Mutational analysis demonstrated focal NF1 copy number loss in PN and MPNST patient plasma but not in healthy controls. Greater genomic instability including alterations associated with malignant transformation (focal copy number gains in chromosome arms 1q, 7p, 8q, 9q, and 17q; focal copy number losses in SUZ12, SMARCA2, CDKN2A/B, and chromosome arms 6p and 9p) was more prominently observed in MPNST plasma. Furthermore, the sum of longest tumor diameters (SLD) visualized by cross-sectional imaging correlated significantly with paired tumor fractions in plasma from MPNST patients (r = 0.39, P = 0.024). On serial analysis, tumor fraction levels in plasma dynamically correlated with treatment response to therapy and minimal residual disease (MRD) detection before relapse. Study limitations include a modest MPNST sample size despite accrual from 2 major referral centers for this rare malignancy, and lack of uniform treatment and imaging protocols representing a real-world cohort. CONCLUSIONS: Tumor fraction levels derived from cfDNA fragment size and copy number alteration analysis of plasma cfDNA using ULP-WGS significantly correlated with MPNST tumor burden, accurately distinguished MPNST from its benign PN precursor, and dynamically correlated with treatment response. In the future, our findings could form the basis for improved early cancer detection and monitoring in high-risk cancer-predisposed populations