A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco- regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints

PSMA-PET based radiotherapy: a review of initial experiences, survey on current practice and future perspectives

Gallium prostate specific membrane antigen (PSMA) ligand positron emission tomography (PET) is an increasingly used imaging modality in prostate cancer, especially in cases of tumor recurrence after curative intended therapy. Owed to the novelty of the PSMA-targeting tracers, clinical evidence on the value of PSMA-PET is moderate but rapidly increasing. State of the art imaging is pivotal for radiotherapy treatment planning as it may affect dose prescription, target delineation and use of concomitant therapy. This review summarizes the evidence on PSMA-PET imaging from a radiation oncologist’s point of view. Additionally a short survey containing twelve examples of patients and 6 additional questions was performed in seven mayor academic centers with experience in PSMA ligand imaging and the findings are reported here

Gastroenteropancreatic Neuroendocrine Tumors: Standardizing Therapy Monitoring with Ga-DOTATOC PET/CT Using the Example of Somatostatin Receptor Radionuclide Therapy

The purpose of this study was to standardize therapy monitoring of hepatic metastases from gastroenteropancreatic neuroendocrine tumors (GEP-NETs) during the course of somatostatin receptor radionuclide therapy (SRRT). In 21 consecutive patients with nonresectable hepatic metastases of GEP-NETs, chromogranin A (CgA) and 68 Ga-DOTATOC PET/CT were compared before and after the last SRRT. On 68 Ga-DOTATOC PET/CT, the maximum standard uptake values (SUV max ) of normal liver and hepatic metastases were calculated. In addition, the volumes of hepatic metastases (volume of interest [VOI]) were measured using four cut-offs to separate normal liver tissue from metastases (SUV max of the normal liver plus 10% [VOI liver+10% ], 20% [VOI liver+20% ], 30% [VOI liver+30% ] and SUV = 10 [VOI 10SUV ]). The SUV max of the normal liver was below 10 (7.2 ± 1.3) in all patients and without significant changes. Overall therapy changes (Δ) per patient (mean [95% CI]) were statistically significant with p < .01 for ΔCgA = −43 (−69 to −17), ΔSUV max = −22 (−29 to −14), and ΔVOI 10SUV = −53 (−68 to −38)% and significant with p < .05 for ΔVOI liver+10% = −29 (−55 to −3)%, ΔVOI liver+20% = −32 (−62 to −2) and ΔVOI liver+30% = −37 (−66 to −8). Correlations were found only between ΔCgA and ΔVOI 10SUV ( r = .595; p < .01), ΔSUV max and ΔVOI 10SUV (0.629, p < .01), and SUV max and ΔSUV max ( r = .446; p < .05). 68 Ga-DOTATOC PET/CT allows volumetric therapy monitoring via an SUV-based cut-off separating hepatic metastases from normal liver tissue (10 SUV recommended)

Hibernoma – two patients with a rare lipoid soft-tissue tumour

Background: Hibernomas are rare benign soft-tissue tumours arising from brown fat tissue. Although imaging characteristics are not specific certain imaging features, common locations and patient demographics may suggest hibernoma as a differential diagnosis. Case presentation: We report on two 48-year-old male patients with hibernoma. The tumour presented with local swelling of the inguinal region in the first patient and was an incidental imaging finding in the second patient. Imaging included magnetic resonance imaging in both patients and computed tomography as well as 18 F-fluorodeoxyglucose positron emission tomography-computed tomography in the second patient. In both cases histological diagnosis was initially based on excisional and needle core biopsy, respectively. Complete surgical resection confirmed the diagnosis of hibernoma thereafter. Conclusion: In soft tissue tumours with fatty components hibernoma may be included into the differential diagnosis. Because of the risk of sampling errors in hibernoma-like tissue components of myxoid and well-differentiated liposarcoma, complete resection is mandatory. This article also reviews the current imaging literature of hibernomas

PSMA-PET/CT-Positive Paget Disease in a Patient with Newly Diagnosed Prostate Cancer: Imaging and Bone Biopsy Findings

A 67-year-old man diagnosed with Gleason score 4+5=9 clinically localized prostate cancer with 68Ga-labeled prostate-specific membrane antigen-targeted ligand positron emission tomography/computed tomography (PSMA-PET/CT) positive Paget bone disease is described. Immunohistochemical staining revealed weak PSMA positivity of the bone lesion supporting the hypothesis that neovasculature might explain positive PSMA-PET/CT findings in Paget disease