Philipp Kickingereder

This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADC_low) was used for further analysis. The predictive ability of ADC_low was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). ADC_low was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADC_low and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADC_low was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADC_low cutoff of 1241*10–6 mm²/s for OS. Thereby, median OS for BEV-patients with ADC_low ≥ 1241 was 10.39 months versus 8.09 months for those with ADC_low < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADC_low ≥ 1241 was 9.80 months versus 7.79 months for those with ADC_low < 1241 (P = 0.054). ADC_low is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADC_low as predictive imaging biomarker could not be confirmed within this phase II/III trial.

This secondary analysis of European Organization for Research and Treatment of Cancer 26101 highlights the biological activity and effectiveness of bevacizumab for extending progression-free surviv... Background Relevance of antiangiogenic treatment with bevacizumab in patients with glioblastoma is controversial because progression-free survival benefit did not translate into an overall survival...

Objectives: To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.Methods: In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient.Results: In the 259 eligible men (median 64 [IQR 61–72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis.Conclusions: U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance.Key Points: • U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment.• Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference.• Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.

Objectives The clinical utility of electronically derived ASPECTS (e-ASPECTS) to quantify signs of acute ischemic infarction could be demonstrated in multiple studies. Here, we aim to clinically validate the impact of CT slice thickness (ST) on the performance of e-ASPECTS software. Methods A consecutive series of n = 258 patients (06/2016 and 01/2019) with middle cerebral artery occlusion and subsequent treatment with mechanical thrombectomy was analyzed. The e-ASPECTS score and acute infarct volumes were calculated from baseline non-contrast CT with a software using 1-mm slice thickness (ST) (defined as ground truth) and axial reconstructions with 2–10-mm ST and correlated with baseline stroke severity (NIHSS) as well as clinical outcome (mRS) using logistic regressions. Results In comparison with the ground truth, significant differences were seen in e-ASPECTS scores with ST > 6 mm (p ≤ 0.031) and infarct volumes with ST > 4 mm (p ≤ 0.001). There was a significant correlation of lower e-ASPECTS and higher acute infarct volumes with increasing baseline NIHSS values for all ST (p ≤ 0.001, respectively), with values derived from 1 mm yielding the highest correlation for both parameters (rho, − 0.38 and 0.31, respectively). Similarly, lower e-ASPECTS and higher acute infarct volumes from all ST were significantly associated with poor outcome after 90 days (p ≤ 0.05, respectively) with values derived from 1-mm ST yielding the highest effects for both parameters (OR, 0.69 [95% CI 0.50–0.88] and 1.27 [95% CI 1.10–1.50], respectively). Conclusions The e-ASPECTS software generates robust values for e-ASPECTS and acute infarct volumes when using ST ≤ 4 mm with ST = 1 mm yielding the best performance for predicting baseline stroke severity and clinical outcome after 90 days. Key Points • Clinical utility of automatically derived ASPECTS from computed tomography scans was shown in patients with acute ischemic stroke and treatment with mechanical thrombectomy. • Thin slices (= 1 mm) had the highest clinical utility in comparison with thicker slices (2–10 mm) by having the strongest correlation with baseline stroke severity and independent effects on clinical outcome after 90 days. • Automatically calculated acute infarct volumes possess clinical utility beyond ASPECTS and should be considered in future studies.

Intrinsic malignant brain tumors, such as glioblastomas are frequently resistant to immune checkpoint blockade (ICB) with few hypermutated glioblastomas showing response. Modeling patient-individual resistance is challenging due to the lack of predictive biomarkers and limited accessibility of tissue for serial biopsies. Here, we investigate resistance mechanisms to anti-PD-1 and anti-CTLA-4 therapy in syngeneic hypermutated experimental gliomas and show a clear dichotomy and acquired immune heterogeneity in ICB-responder and non-responder tumors. We made use of this dichotomy to establish a radiomic signature predicting tumor regression after pseudoprogression induced by ICB therapy based on serial magnetic resonance imaging. We provide evidence that macrophage-driven ICB resistance is established by CD4 T cell suppression and T_reg expansion in the tumor microenvironment via the PD-L1/PD-1/CD80 axis. These findings uncover an unexpected heterogeneity of response to ICB in strictly syngeneic tumors and provide a rationale for targeting PD-L1-expressing tumor-associated macrophages to overcome resistance to ICB.

Objectives Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. Methods A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. Results The F1 score was maximized in the training set at a detection threshold of 7 mm³ for T2/FLAIR lesions and 14 mm³ for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. Conclusions Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. Key Points • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Park JE, et al. Korean J Radiol. 2020 Jul;21:e114. https://doi.org/10.3348/kjr.2019.0847

This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma. Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed. The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s kappa = 0.75 [95% CI, 0.57–0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005). This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

A deep learning algorithm (U-Net) trained to evaluate T2-weighted and diffusion MRI had similar detection of clinically significant prostate cancer to clinical Prostate Imaging Reporting and Data S... Background Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretat...