2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. / 2-Hydroxyglutarat als MR-spektroskopisch erfassbarer Prädiktor einer IDH-Mutation bei Gliomen.

The mutated enzyme isocitrate dehydrogenase (IDH) 1 and 2 has been detected in various tumor entities such as gliomas and can convert α-ketoglutarate into the oncometabolite 2-hydroxyglutarate (2-HG). This neuro-oncologically significant metabolic product can be detected by MR spectroscopy and is therefore suitable for noninvasive glioma classification and therapy monitoring.This paper provides an up-to-date overview of the methodology and relevance of 1H-MR spectroscopy (MRS) in the oncological primary and follow-up diagnosis of gliomas. The possibilities and limitations of this MR spectroscopic examination are evaluated on the basis of the available literature.By detecting 2-HG, MRS can in principle offer a noninvasive alternative to immunohistological analysis thus avoiding surgical intervention in some cases. However, in addition to an adapted and optimized examination protocol, the individual measurement conditions in the examination region are of decisive importance. Due to the inherently small signal of 2-HG, unfavorable measurement conditions can influence the reliability of detection. · MR spectroscopy enables the non-invasive detection of 2-hydroxyglutarate.. · The measurement of this metabolite allows the detection of an IDH mutation in gliomas.. · The choice of MR examination method is particularly important.. · Detection reliability is influenced by glioma size, necrotic tissue and the existing measurement conditions.. · Bauer J, Raum HN, Kugel H et al. 2-Hydroxyglutarate as an MR spectroscopic predictor of an IDH mutation in gliomas. Fortschr Röntgenstr 2024; DOI 10.1055/a-2285-4923.

Radiomics-Based Prediction Model for Outcome of Radioembolization in Metastatic Colorectal Cancer.

PURPOSE: To evaluate the benefit of a contrast-enhanced computed tomography (CT) radiomics-based model for predicting response and survival in patients with colorectal liver metastases treated with transarterial Yttrium-90 radioembolization (TARE). MATERIALS AND METHODS: Fifty-one patients who underwent TARE were included in this single-center retrospective study. Response to treatment was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) at 3-month follow-up. Patients were stratified as responders (complete/partial response and stable disease, n = 24) or non-responders (progressive disease, n = 27). Radiomic features (RF) were extracted from pre-TARE CT after segmentation of the liver tumor volume. A model was built based on a radiomic signature consisting of reliable RFs that allowed classification of response using multivariate logistic regression. Patients were assigned to high- or low-risk groups for disease progression after TARE according to a cutoff defined in the model. Kaplan-Meier analysis was performed to analyze survival between high- and low-risk groups. RESULTS: Two independent RF [Energy, Maximal Correlation Coefficient (MCC)], reflecting tumor heterogeneity, discriminated well between responders and non-responders. In particular, patients with higher magnitude of voxel values in an image (Energy), and texture complexity (MCC), were more likely to fail TARE. For predicting treatment response, the area under the receiver operating characteristic curve of the radiomics-based model was 0.75 (95% CI 0.48-1). The high-risk group had a shorter overall survival than the low-risk group (3.4 vs. 6.4 months, p < 0.001). CONCLUSION: Our CT radiomics model may predict the response and survival outcome by quantifying tumor heterogeneity in patients treated with TARE for colorectal liver metastases.

A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder.

Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified. Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD. Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023. Exposure: Patients with MDD and healthy controls. Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression. Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups. Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.

Breast cancer screening with digital breast tomosynthesis: Is independent double reading still required? / Brustkrebs-Screening mit digitaler Brust-Tomosynthese: Ist weiterhin eine unabhängige Doppelbefundung erforderlich?

PURPOSE: The European guidelines recommend independent double reading in mammography screening programs. The prospective randomized controlled trial TOSYMA tested the superiority of digital breast tomosynthesis and synthetic mammography (DBT+SM) over digital mammography (DM) for invasive breast cancer detection. This sub-analysis compares the true-positive readings of screening-detected breast cancers resulting from independent double readings in the two trial arms. MATERIALS AND METHODS: The 1:1 randomized TOSYMA trial was executed in 17 screening units between 07/2018 and 12/2020. This sub-analysis included 49,762 women in the test arm (DBT+SM) and 49,796 women in the control arm (DM). The true-positive reading results (invasive breast cancers and ductal carcinoma in situ) from 83 readers were determined and merged in a double reading result. RESULTS: DBT+SM screening detected 416 women with breast cancer and DM screening detected 306. Double readings of DBT+SM examinations led to a single true-positive together with a single false-negative result in 26.9 % of cancer cases (112/416), and in 22.2 % of cases (68/306) in the DM examinations. The cancer detection rate with discordant reading results was 2.3 per 1,000 women screened with DBT+SM and 1.4 per 1,000 with DM. Discordant reading results occurred most often for invasive breast cancers [DBT+SM 75.9 % (85/112), DM 67.6 % (46/68)], category T1 [DBT+SM 67.9 % (76/112), DM 55.9 % (38/68)], and category 4a [DBT+SM: 67.6 % (73/112); DM: 84.6 % (55/68)]. CONCLUSION: The higher breast cancer detection rate with DBT screening includes a relevant percentage of breast cancers that were only detected by one reader in an independent double reading. As in digital mammography, independent double reading continues to be justified in screening with digital breast tomosynthesis. KEY POINTS: · The percentages of discordant cancer reading results were 26.9 % and 22.2 % for DBT+SM and DM, respectively.. · The single true-positive detection rate was 2.3 ‰ for DBT+ SM and 1.4 ‰ for DM.. · A relevant proportion of screening-detected cancers resulted from a single true-positive reading.. CITATION FORMAT: · Weigel S, Hense HW, Weyer-Elberich V et al. Breast cancer screening with digital breast tomosynthesis: Is independent double reading still required?. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2216-1109.

Digital Breast Tomosynthesis versus Digital Mammography for Detection of Early-Stage Cancers Stratified by Grade: A TOSYMA Subanalysis.

Background Breast cancer screening with digital breast tomosynthesis (DBT) plus synthesized mammography (SM) increases invasive tumor detection compared with digital mammography (DM). However, it is not known how the prognostic characteristics of the cancers detected with the two screening approaches differ. Purpose To compare invasive breast cancers detected with DBT plus SM (test arm) versus DM (control arm) screening with regard to tumor stage, histologic grade, patient age, and breast density. Materials and Methods This exploratory subanalysis of the Tomosynthesis plus Synthesized Mammography (TOSYMA) study, which is a multicenter randomized controlled trial embedded in the German mammography screening program, recruited women aged 50-70 years from July 2018 to December 2020. It compared invasive cancer detection rates (iCDRs), rate differences, and odds ratios (ORs) between the arms stratified by Union for International Cancer Control (UICC) stage (I vs II-IV), histologic grade (1 vs 2 or 3), age group (50-59 vs 60-70 years), and Breast Imaging Reporting and Data System categories of breast density (A or B vs C or D). Results In total, 49 462 (median age, 57 years [IQR, 53-62 years]) and 49 669 (median age, 57 years [IQR, 53-62 years]) participants were allocated to DBT plus SM and DM screening, respectively. The iCDR of stage I tumors with DBT plus SM was 51.6 per 10 000 women (255 of 49 462) and with DM it was 30.0 per 10 000 women (149 of 49 669). DBT plus SM depicted more stage I tumors with grade 2 or 3 (166 of 49 462, 33.7 per 10 000 women) than DM (106 of 49 669, 21.3 per 10 000 women; rate difference, +12.3 per 10 000 women [95% CI: 0.3, 24.9]; OR, 1.6 [95% CI: 0.9, 2.7]). DBT plus SM achieved the highest iCDR of stage I tumors with grade 2 or 3 among women aged 60-70 years with dense breasts (41 of 7364, 55.4 per 10 000 women; rate difference, +21.6 per 10 000 women [95% CI: -21.1, 64.3]; OR, 1.6 [95% CI: 0.6, 4.5]). Conclusion DBT plus SM screening appears to lead to higher detection of early-stage invasive breast cancers of grade 2 or 3 than DM screening, with the highest rate among women aged 60-70 years with dense breasts. Clinical trial registration no. NCT03377036 © RSNA, 2023 See also the editorial by Ha and Chang in this issue.

Robustness of apparent diffusion coefficient-based lymph node classification for diagnosis of prostate cancer metastasis.

OBJECTIVES: The aim of this proof-of-principle study combining data analysis and computer simulation was to evaluate the robustness of apparent diffusion coefficient (ADC) values for lymph node classification in prostate cancer under conditions comparable to clinical practice. MATERIALS AND METHODS: To assess differences in ADC and inter-rater variability, ADC values of 359 lymph nodes in 101 patients undergoing simultaneous prostate-specific membrane antigen (PSMA)-PET/MRI were retrospectively measured by two blinded readers and compared in a node-by-node analysis with respect to lymph node status. In addition, a phantom and 13 patients with 86 lymph nodes were prospectively measured on two different MRI scanners to analyze inter-scanner agreement. To estimate the diagnostic quality of the ADC in real-world application, a computer simulation was used to emulate the blurring caused by scanner and reader variability. To account for intra-individual correlation, the statistical analyses and simulations were based on linear mixed models. RESULTS: The mean ADC of lymph nodes showing PSMA signals in PET was markedly lower (0.77 × 10-3 mm2/s) compared to inconspicuous nodes (1.46 × 10-3 mm2/s, p < 0.001). High inter-reader agreement was observed for ADC measurements (ICC 0.93, 95%CI [0.92, 0.95]). Good inter-scanner agreement was observed in the phantom study and confirmed in vivo (ICC 0.89, 95%CI [0.84, 0.93]). With a median AUC of 0.95 (95%CI [0.92, 0.97]), the simulation study confirmed the diagnostic potential of ADC for lymph node classification in prostate cancer. CONCLUSION: Our model-based simulation approach implicates a high potential of ADC for lymph node classification in prostate cancer, even when inter-rater and inter-scanner variability are considered. CLINICAL RELEVANCE STATEMENT: The ADC value shows a high diagnostic potential for lymph node classification in prostate cancer. The robustness to scanner and reader variability implicates that this easy to measure and widely available method could be readily integrated into clinical routine. KEY POINTS: • The diagnostic value of the apparent diffusion coefficient (ADC) for lymph node classification in prostate cancer is unclear in the light of inter-rater and inter-scanner variability. • Metastatic and inconspicuous lymph nodes differ significantly in ADC, resulting in a high diagnostic potential that is robust to inter-scanner and inter-rater variability. • ADC has a high potential for lymph node classification in prostate cancer that is maintained under conditions comparable to clinical practice.

Diagnostic approach to splenic lesions.

BACKGROUND: Splenic lesions are rare and mostly incidental findings on cross-sectional imaging. Most lesions are of benign nature and can be correctly identified based on imaging characteristics. Further, invasive evaluation is only necessary in cases of splenic lesions with uncertain or potentially malignant etiology. METHOD: While in most cases a correct diagnosis can be made from computed tomography (CT), (additional) magnetic resonance imaging (MRI) can aid in the identification of lesions. As these lesions are rare, only a few of the differential diagnoses are regularly diagnosed in the clinical routine. RESULT AND CONCLUSION: This review presents the differential diagnoses of splenic lesions, including imaging characteristics and a flowchart to determine the right diagnosis. In conjunction with laboratory results and clinical symptoms, histological workup is necessary only in a few cases, especially in incidental findings. In these cases, image-guided biopsies should be preferred over splenectomy, if possible. KEY POINTS: · Splenic lesions are rare and are usually incidental findings on abdominal imaging. · CT imaging and MRI imaging are the diagnostic tools of choice for the further workup of splenic lesions. · Based on their image morphological characteristics, a large number of splenic lesions can be assigned to one entity and do not need histological analysis.

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.

PURPOSE: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. METHODS: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. RESULTS: A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. CONCLUSIONS: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.

Multi-organ Radiomics-Based Prediction of Future Remnant Liver Hypertrophy Following Portal Vein Embolization.

BACKGROUND: Portal vein embolization (PVE) is used to induce remnant liver hypertrophy prior to major hepatectomy. The purpose of this study was to evaluate the predictive value of baseline computed tomography (CT) data for future remnant liver (FRL) hypertrophy after PVE. METHODS: In this retrospective study, all consecutive patients undergoing right-sided PVE with or without hepatic vein embolization between 2018 and 2021 were included. CT volumetry was performed before and after PVE to assess standardized FRL volume (sFRLV). Radiomic features were extracted from baseline CT after segmenting liver (without tumor), spleen and bone marrow. For selecting features that allow classification of response (hypertrophy ≥ 1.33), a stepwise dimension reduction was performed. Logistic regression models were fitted and selected features were tested for their predictive value. Decision curve analysis was performed on the test dataset. RESULTS: A total of 53 patients with liver tumor were included in this study. sFRLV increased significantly after PVE, with a mean hypertrophy of FRL of 1.5 ± 0.3-fold. sFRLV hypertrophy ≥ 1.33 was reached in 35 (66%) patients. Three independent radiomic features, i.e. liver-, spleen- and bone marrow-associated, differentiated well between responders and non-responders. A logistic regression model revealed the highest accuracy (area under the curve 0.875) for the prediction of response, with sensitivity of 1.0 and specificity of 0.5. Decision curve analysis revealed a positive net benefit when applying the model. CONCLUSIONS: This proof-of-concept study provides first evidence of a potential predictive value of baseline multi-organ radiomics CT data for FRL hypertrophy after PVE.

Long-term effects of electroconvulsive therapy on brain structure in major depression.

BACKGROUND: Magnetic resonance imaging (MRI) studies on major depressive disorder (MDD) have predominantly found short-term electroconvulsive therapy (ECT)-related gray matter volume (GMV) increases, but research on the long-term stability of such changes is missing. Our aim was to investigate long-term GMV changes over a 2-year period after ECT administration and their associations with clinical outcome. METHODS: In this nonrandomized longitudinal study, patients with MDD undergoing ECT (n = 17) are assessed three times by structural MRI: Before ECT (t0), after ECT (t1) and 2 years later (t2). A healthy (n = 21) and MDD non-ECT (n = 33) control group are also measured three times within an equivalent time interval. A 3(group) × 3(time) ANOVA on whole-brain level and correlation analyses with clinical outcome variables is performed. RESULTS: Analyses yield a significant group × time interaction (pFWE < 0.001) resulting from significant volume increases from t0 to t1 and decreases from t1 to t2 in the ECT group, e.g., in limbic areas. There are no effects of time in both control groups. Volume increases from t0 to t1 correlate with immediate and delayed symptom increase, while volume decreases from t1 to t2 correlate with long-term depressive outcome (all p ⩽ 0.049). CONCLUSIONS: Volume increases induced by ECT appear to be a transient phenomenon as volume strongly decreased 2 years after ECT. Short-term volume increases are associated with less symptom improvement suggesting that the antidepressant effect of ECT is not due to volume changes. Larger volume decreases are associated with poorer long-term outcome highlighting the interplay between disease progression and structural changes.

Multiparametric chemical exchange saturation transfer MRI detects metabolic changes in breast cancer following immunotherapy.

BACKGROUND: With metabolic alterations of the tumor microenvironment (TME) contributing to cancer progression, metastatic spread and response to targeted therapies, non-invasive and repetitive imaging of tumor metabolism is of major importance. The purpose of this study was to investigate whether multiparametric chemical exchange saturation transfer magnetic resonance imaging (CEST-MRI) allows to detect differences in the metabolic profiles of the TME in murine breast cancer models with divergent degrees of malignancy and to assess their response to immunotherapy. METHODS: Tumor characteristics of highly malignant 4T1 and low malignant 67NR murine breast cancer models were investigated, and their changes during tumor progression and immune checkpoint inhibitor (ICI) treatment were evaluated. For simultaneous analysis of different metabolites, multiparametric CEST-MRI with calculation of asymmetric magnetization transfer ratio (MTRasym) at 1.2 to 2.0 ppm for glucose-weighted, 2.0 ppm for creatine-weighted and 3.2 to 3.6 ppm for amide proton transfer- (APT-) weighted CEST contrast was conducted. Ex vivo validation of MRI results was achieved by 1H nuclear magnetic resonance spectroscopy, matrix-assisted laser desorption/ionization mass spectrometry imaging with laser postionization and immunohistochemistry. RESULTS: During tumor progression, the two tumor models showed divergent trends for all examined CEST contrasts: While glucose- and APT-weighted CEST contrast decreased and creatine-weighted CEST contrast increased over time in the 4T1 model, 67NR tumors exhibited increased glucose- and APT-weighted CEST contrast during disease progression, accompanied by decreased creatine-weighted CEST contrast. Already three days after treatment initiation, CEST contrasts captured response to ICI therapy in both tumor models. CONCLUSION: Multiparametric CEST-MRI enables non-invasive assessment of metabolic signatures of the TME, allowing both for estimation of the degree of tumor malignancy and for assessment of early response to immune checkpoint inhibition.

Comparison of MRI Sequences to Predict ATRX Status Using Radiomics-Based Machine Learning.

ATRX is an important molecular marker according to the 2021 WHO classification of adult-type diffuse glioma. We aim to predict the ATRX mutation status non-invasively using radiomics-based machine learning models on MRI and to determine which MRI sequence is best suited for this purpose. In this retrospective study, we used MRI images of patients with histologically confirmed glioma, including the sequences T1w without and with the administration of contrast agent, T2w, and the FLAIR. Radiomics features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects. Feature preselection and subsequent model development were performed using Lasso regression. The T2w sequence was found to be the most suitable and the FLAIR sequence the least suitable for predicting ATRX mutations using radiomics-based machine learning models. For the T2w sequence, our seven-feature model developed with Lasso regression achieved a mean AUC of 0.831, a mean accuracy of 0.746, a mean sensitivity of 0.772, and a mean specificity of 0.697. In conclusion, for the prediction of ATRX mutation using radiomics-based machine learning models, the T2w sequence is the most suitable among the commonly used MRI sequences.

Use Test of Automated Machine Learning in Cancer Diagnostics.

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.

Vascular response patterns to targeted therapies in murine breast cancer models with divergent degrees of malignancy.

BACKGROUND: Response assessment of targeted cancer therapies is becoming increasingly challenging, as it is not adequately assessable with conventional morphological and volumetric analyses of tumor lesions. The tumor microenvironment is particularly constituted by tumor vasculature which is altered by various targeted therapies. The aim of this study was to noninvasively assess changes in tumor perfusion and vessel permeability after targeted therapy in murine models of breast cancer with divergent degrees of malignancy. METHODS: Low malignant 67NR or highly malignant 4T1 tumor-bearing mice were treated with either the multi-kinase inhibitor sorafenib or immune checkpoint inhibitors (ICI, combination of anti-PD1 and anti-CTLA4). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with i.v. injection of albumin-binding gadofosveset was conducted on a 9.4 T small animal MRI. Ex vivo validation of MRI results was achieved by transmission electron microscopy, immunohistochemistry and laser ablation-inductively coupled plasma-mass spectrometry. RESULTS: Therapy-induced changes in tumor vasculature differed between low and highly malignant tumors. Sorafenib treatment led to decreased tumor perfusion and endothelial permeability in low malignant 67NR tumors. In contrast, highly malignant 4T1 tumors demonstrated characteristics of a transient window of vascular normalization with an increase in tumor perfusion and permeability early after therapy initiation, followed by decreased perfusion and permeability parameters. In the low malignant 67NR model, ICI treatment also mediated vessel-stabilizing effects with decreased tumor perfusion and permeability, while ICI-treated 4T1 tumors exhibited increasing tumor perfusion with excessive vascular leakage. CONCLUSION: DCE-MRI enables noninvasive assessment of early changes in tumor vasculature after targeted therapies, revealing different response patterns between tumors with divergent degrees of malignancy. DCE-derived tumor perfusion and permeability parameters may serve as vascular biomarkers that allow for repetitive examination of response to antiangiogenic treatment or immunotherapy.

Radiomics of Tumor Heterogeneity in 18F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer.

We aimed to evaluate the predictive and prognostic value of baseline 18F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" (n = 33) and "non-responder" (n = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for "PET-Skewness" and 0.75 predicting overall progression for "PET-Median". In terms of progression-free survival analysis, patients with a lower value of PET-Skewness (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; p < 0.001) and higher value of PET-Median (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; p < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.

Association of hospitalization with structural brain alterations in patients with affective disorders over nine years.

Repeated hospitalizations are a characteristic of severe disease courses in patients with affective disorders (PAD). To elucidate how a hospitalization during a nine-year follow-up in PAD affects brain structure, a longitudinal case-control study (mean [SD] follow-up period 8.98 [2.20] years) was conducted using structural neuroimaging. We investigated PAD (N = 38) and healthy controls (N = 37) at two sites (University of Münster, Germany, Trinity College Dublin, Ireland). PAD were divided into two groups based on the experience of in-patient psychiatric treatment during follow-up. Since the Dublin-patients were outpatients at baseline, the re-hospitalization analysis was limited to the Münster site (N = 52). Voxel-based morphometry was employed to examine hippocampus, insula, dorsolateral prefrontal cortex and whole-brain gray matter in two models: (1) group (patients/controls)×time (baseline/follow-up) interaction; (2) group (hospitalized patients/not-hospitalized patients/controls)×time interaction. Patients lost significantly more whole-brain gray matter volume of superior temporal gyrus and temporal pole compared to HC (pFWE = 0.008). Patients hospitalized during follow-up lost significantly more insular volume than healthy controls (pFWE = 0.025) and more volume in their hippocampus compared to not-hospitalized patients (pFWE = 0.023), while patients without re-hospitalization did not differ from controls. These effects of hospitalization remained stable in a smaller sample excluding patients with bipolar disorder. PAD show gray matter volume decline in temporo-limbic regions over nine years. A hospitalization during follow-up comes with intensified gray matter volume decline in the insula and hippocampus. Since hospitalizations are a correlate of severity, this finding corroborates and extends the hypothesis that a severe course of disease has detrimental long-term effects on temporo-limbic brain structure in PAD.

Profiling specific cell populations within the inflammatory tumor microenvironment by oscillating-gradient diffusion-weighted MRI.

BACKGROUND: The inflammatory tumor microenvironment (TME) is formed by various immune cells, being closely associated with tumorigenesis. Especially, the interaction between tumor-infiltrating T-cells and macrophages has a crucial impact on tumor progression and metastatic spread. The purpose of this study was to investigate whether oscillating-gradient diffusion-weighted MRI (OGSE-DWI) enables a cell size-based discrimination between different cell populations of the TME. METHODS: Sine-shaped OGSE-DWI was combined with the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) approach to measure microscale diffusion distances, here relating to cell sizes. The accuracy of IMPULSED-derived cell radii was evaluated using in vitro spheroid models, consisting of either pure cancer cells, macrophages, or T-cells. Subsequently, in vivo experiments aimed to assess changes within the TME and its specific immune cell composition in syngeneic murine breast cancer models with divergent degrees of malignancy (4T1, 67NR) during tumor progression, clodronate liposome-mediated depletion of macrophages, and immune checkpoint inhibitor (ICI) treatment. Ex vivo analysis of IMPULSED-derived cell radii was conducted by immunohistochemical wheat germ agglutinin staining of cell membranes, while intratumoral immune cell composition was analyzed by CD3 and F4/80 co-staining. RESULTS: OGSE-DWI detected mean cell radii of 8.8±1.3 µm for 4T1, 8.2±1.4 µm for 67NR, 13.0±1.7 for macrophage, and 3.8±1.8 µm for T-cell spheroids. While T-cell infiltration during progression of 4T1 tumors was observed by decreasing mean cell radii from 9.7±1.0 to 5.0±1.5 µm, increasing amount of intratumoral macrophages during progression of 67NR tumors resulted in increasing mean cell radii from 8.9±1.2 to 12.5±1.1 µm. After macrophage depletion, mean cell radii decreased from 6.3±1.7 to 4.4±0.5 µm. T-cell infiltration after ICI treatment was captured by decreasing mean cell radii in both tumor models, with more pronounced effects in the 67NR tumor model. CONCLUSIONS: OGSE-DWI provides a versatile tool for non-invasive profiling of the inflammatory TME by assessing the dominating cell type T-cells or macrophages.

Towards a network control theory of electroconvulsive therapy response.

Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma.

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder.

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.