Bone metastases with post-treatment intralesional fatty content of the spine: imaging features from T1-weighted imaging with CT finding correlations

Background Despite post-treatment intralesional fatty content (PIFAT) in bone metastases indicating a healing processes after treatment, the imaging features of PIFAT have not been studied in detail. Purpose To analyze imaging features from T1-weighted (T1W) imaging with computed tomography (CT) finding correlations in bone metastases with PIFAT of the spine. Material and Methods A total of 29 bone metastases with PIFAT were analyzed with T1W and CT images before and after treatment. On T1W imaging after treatment, the lesions were categorized into three types according to fat distribution patterns. CT attenuation changes after treatment were also evaluated. According to the MD Anderson (MDA) criteria, response types for all lesions were obtained on magnetic resonance (MR) and CT images. Results The types from T1W imaging in bone metastases with PIFAT were as follows: 14 with a return to totally normal marrow signal intensity within the lesion; 13 with an inhomogeneous patchy pattern in the lesion; and two with a peripheral halo of fatty marrow or peripheral fat signal intensity foci in the lesion. Among bone metastases with PIFAT, 93.1% showed osteosclerotic changes in this study. According to the MDA criteria, the concordance between the response types of the MR and CT images was 57.2%. Conclusion Knowledge of imaging features from T1W imaging with CT correlation in bone metastases with PIFAT is important for the accurate interpretation of post-treatment MR and CT studies. Both MR and CT images have a complementary value regarding the post-treatment evaluation of bone metastases with PIFAT.

EANM recommendations based on systematic analysis of small animal radionuclide imaging in inflammatory musculoskeletal diseases

Inflammatory musculoskeletal diseases represent a group of chronic and disabling conditions that evolve from a complex interplay between genetic and environmental factors that cause perturbations in innate and adaptive immune responses. Understanding the pathogenesis of inflammatory musculoskeletal diseases is, to a large extent, derived from preclinical and basic research experiments. In vivo molecular imaging enables us to study molecular targets and to measure biochemical processes non-invasively and longitudinally, providing information on disease processes and potential therapeutic strategies, e.g. efficacy of novel therapeutic interventions, which is of complementary value next to ex vivo (post mortem) histopathological analysis and molecular assays. Remarkably, the large body of preclinical imaging studies in inflammatory musculoskeletal disease is in contrast with the limited reports on molecular imaging in clinical practice and clinical guidelines. Therefore, in this EANM-endorsed position paper, we performed a systematic review of the preclinical studies in inflammatory musculoskeletal diseases that involve radionuclide imaging, with a detailed description of the animal models used. From these reflections, we provide recommendations on what future studies in this field should encompass to facilitate a greater impact of radionuclide imaging techniques on the translation to clinical settings.

The added value of PSMA PET/MR radiomics for prostate cancer staging

Purpose To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. Methods Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1–3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student’s t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). Results All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. Conclusion All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.

Identification of high risk clinical and imaging features for intracranial artery dissection using high-resolution cardiovascular magnetic resonance

Background Intracranial artery dissection (IAD) often causes headache and cerebral vascular ischemic events. The imaging characteristics of IAD remain unclear. This study aims to characterize the appearance of culprit and non-culprit IAD using high-resolution cardiovascular magnetic resonance imaging (hrCMR) and quantify the incremental value of hrCMR in identifying higher risk lesions. Methods Imaging data from patients who underwent intervention examination or treatment using digital subtraction angiography (DSA) and hrCMR using a 3 T CMR system within 30 days after the onset of neurological symptoms were collected. The CMR protocol included diffusion-weighted imaging (DWI), black blood T1-, T2- and contrast-enhanced T1-weighted sequences. Lesions were classified as culprit and non-culprit according to imaging findings and patient clinical presentations. Univariate and multivariate analyses were performed to assess the difference between culprit and non-culprit lesions and complementary value of hrCMR in identifying higher risk lesions. Results In total, 75 patients were included in this study. According to the morphology, lesions could be classified into five types: Type I, classical dissection (n = 50); Type II, fusiform aneurysm (n = 1); Type III, long dissected aneurysm (n = 3); Type IV, dolichoectatic dissecting aneurysm (n = 9) and Type V, saccular aneurysm (n = 12). Regression analyses showed that age and hypertension were both associated with culprit lesions (age: OR, 0.83; 95% CI 0.75–0.92; p < 0.001 and hypertension: OR, 66.62; 95% CI 5.91–751.11; p = 0.001). Hematoma identified by hrCMR was significantly associated with culprit lesions (OR, 16.80; 95% CI 1.01–280.81; p = 0.037). Moreover, 17 cases (16 lesions were judged to be culprit) were diagnosed as IAD but not visible in DSA and 15 were Type I lesion. Conclusion hrCMR is helpful in visualizing and characterizing IAD. It provides a significant complementary value over DSA for the diagnosis of IAD.

Combining hematoxylin and eosin (H&E) stained images and RNA sequencing (RNA-Seq) data to predict overall survival (OS) in patients with non-small cell lung cancer (NSCLC).

1547 Background: IMpower150 was a phase 3 clinical trial that evaluated the efficacy of atezolizumab in patients with metastatic nonsquamous NSCLC; it demonstrated no significant OS benefit in the ACP (atezolizumab+carboplatin+paclitaxel) arm vs the BCP (bevacizumab+carboplatin+paclitaxel) control arm (hazard ratio [HR]=0.85; 95%CI, 0.71-1.03). The objective of this analysis was to identify a subpopulation of patients that benefits from ACP using H&E stained images and RNA-Seq data. Methods: Spatial statistics algorithms were applied to the coordinates of tumor cells and lymphocytes of the H&E stained images to capture spatial heterogeneity of the tumor microenvironment. The normalized and log-transformed RNA-Seq data underwent a nested feature selection procedure using a Cox proportional hazard model with L1 regularization and stability selection. Cutoffs for gene selection were determined using a permutation strategy with a false discovery rate <0.001. To investigate the association between the 41 derived spatial features, significant genes and OS, a Cox proportional hazard model with L2 regularization was fitted only for the ACP group. Survival groups were further identified using nested Monte Carlo Cross Validation to prevent over-fitting. Results: A total of 236 ACP and 235 BCP patients who had both H&E stained images and RNA-Seq data were analyzed. In the predicted long survival group, ACP patients had significantly longer median OS vs BCP patients based on H&E stained images (HR=0.61; 95%CI, 0.41-0.90; P=0.013) and RNA-Seq data (HR=0.64; 95%CI, 0.41-0.99; P=0.042). The combination of both modalities further improved the OS benefit between the arms (HR=0.44; 95%CI, 0.27-0.73; P=0.001). Data-driven selection of genes relevant for the prediction of OS included MAML3, AC024475.4, RGPD1, LCE3D and AC004156.1. Conclusions: Our approach was able to stratify a subpopulation of patients that significantly benefited from ACP compared with BCP treatment, particularly when integrating both H&E stained images and RNA-Seq data, which demonstrated the complementary value of both modalities. Our results could inform the development of a companion diagnostic that predicts individualized treatment response. Clinical trial information: NCT02366143.

Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods.Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival.Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p &lt; 0.001) for abdominal imaging, and 0.67 AUC (p &lt; 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p &lt; 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p &lt; 0.001), suggesting its complementary value in current clinical settings.Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.

A Combined Two-mRNA Signature Associated With PD-L1 and Tumor Mutational Burden for Prognosis of Lung Adenocarcinoma

Due to biological heterogeneity, lung adenocarcinoma (LUAD) patients with the same stage may exhibit variable responses to immunotherapy and a wide range of outcomes. It is urgent to seek a biomarker that can predict the prognosis and response to immunotherapy in these patients. In this study, we identified two genes (ANLN and ARNTL2) from multiple gene expression data sets, and developed a two-mRNA-based signature that can effectively distinguish high- and low-risk patients and predict patients’ response to immunotherapy. Furthermore, taking full advantage of the complementary value of clinical and molecular features, we combined the immune prognostic signature with clinical features to construct and validate a nomogram that can predict the probability of high tumor mutational burden (&gt;10 mutations per megabyte). This may improve the estimation of immunotherapy response in LUAD patients, and provide a new perspective for clinical screening of immunotherapy beneficiaries.

Transparency and accountability mechanisms for disaster management funds: A case study

This research was conducted to analyze the improvement in the transparency and accountability mechanism for the management of drought disaster relief funds in the cultural aspect. This research was conducted in the Yogyakarta Special Region Fast Response Foundation (ACT DIY) which is engaged in the management of disaster aid funds. The research method used was a qualitative method with primary data by interviews, and secondary data in the form of documents obtained through the official website of ACT, social media, and supporting documents directly requested from the source. The data analysis techniques employed were three stages of coding; initial coding, axial coding, and selective coding, and continued by constructing analytical maps, matrix coding queries, and framework matrices. In order to manage, integrate, test, and search for patterns and more detailed relationships, NVivo 11 software was employed for assistance. The results demonstrate that understanding community culture is a complementary value in improving the transparency and accountability of a foundation which can be a new strategy in improving transparency and accountability in similar foundations and especially the Yogyakarta Special Region's ACT. Similar research by extending community culture through different cultural theories can also be an opportunity for future research to improve transparency and accountability in managing disaster relief funds.