A CT-based radiomics model to detect prostate cancer lymph node metastases in PSMA radioguided surgery patients.

PURPOSE: In recurrent prostate carcinoma, determination of the site of recurrence is crucial to guide personalized therapy. In contrast to prostate-specific membrane antigen (PSMA)-positron emission tomography (PET) imaging, computed tomography (CT) has only limited capacity to detect lymph node metastases (LNM). We sought to develop a CT-based radiomic model to predict LNM status using a PSMA radioguided surgery (RGS) cohort with histological confirmation of all suspected lymph nodes (LNs). METHODS: Eighty patients that received RGS for resection of PSMA PET/CT-positive LNMs were analyzed. Forty-seven patients (87 LNs) that received inhouse imaging were used as training cohort. Thirty-three patients (62 LNs) that received external imaging were used as testing cohort. As gold standard, histological confirmation was available for all LNs. After preprocessing, 156 radiomic features analyzing texture, shape, intensity, and local binary patterns (LBP) were extracted. The least absolute shrinkage and selection operator (radiomic models) and logistic regression (conventional parameters) were used for modeling. RESULTS: Texture and shape features were largely correlated to LN volume. A combined radiomic model achieved the best predictive performance with a testing-AUC of 0.95. LBP features showed the highest contribution to model performance. This model significantly outperformed all conventional CT parameters including LN short diameter (AUC 0.84), LN volume (AUC 0.80), and an expert rating (AUC 0.67). In lymph node-specific decision curve analysis, there was a clinical net benefit above LN short diameter. CONCLUSION: The best radiomic model outperformed conventional measures for detection of LNM demonstrating an incremental value of radiomic features.

Stereotactic body radiotherapy for pancreatic cancer - A systematic review of prospective data.

Purpose: This systematic review aims to comprehensively summarize the current prospective evidence regarding Stereotactic Body Radiotherapy (SBRT) in various clinical contexts for pancreatic cancer including its use as neoadjuvant therapy for borderline resectable pancreatic cancer (BRPC), induction therapy for locally advanced pancreatic cancer (LAPC), salvage therapy for isolated local recurrence (ILR), adjuvant therapy after radical resection, and as a palliative treatment. Special attention is given to the application of magnetic resonance-guided radiotherapy (MRgRT). Methods: Following PRISMA guidelines, a systematic review of the Medline database via PubMed was conducted focusing on prospective studies published within the past decade. Data were extracted concerning study characteristics, outcome measures, toxicity profiles, SBRT dosage and fractionation regimens, as well as additional systemic therapies. Results and conclusion: 31 studies with in total 1,571 patients were included in this review encompassing 14 studies for LAPC, 9 for neoadjuvant treatment, 2 for adjuvant treatment, 2 for ILR, with an additional 4 studies evaluating MRgRT. In LAPC, SBRT demonstrates encouraging results, characterized by favorable local control rates. Several studies even report conversion to resectable disease with substantial resection rates reaching 39%. The adoption of MRgRT may provide a solution to the challenge to deliver ablative doses while minimizing severe toxicities. In BRPC, select prospective studies combining preoperative ablative-dose SBRT with modern induction systemic therapies have achieved remarkable resection rates of up to 80%. MRgRT also holds potential in this context. Adjuvant SBRT does not appear to confer relevant advantages over chemotherapy. While prospective data for SBRT in ILR and for palliative pain relief are limited, they corroborate positive findings from retrospective studies.

Tumor grading of soft tissue sarcomas using MRI-based radiomics.

BACKGROUND: Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS. METHODS: The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects. FINDINGS: Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone. INTERPRETATION: MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas. FUND: The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.