Molecular Urothelial Tumor Cell Subtypes Remain Stable During Metastatic Evolution.

Urothelial cancer (UC) care is moving toward precision oncology. For tumor biology-driven treatment of metastatic UC (mUC), molecular subtypes play a crucial role. However, it is not known whether subtypes change during metastatic evolution. To address this, we analyzed a UC progression cohort (N = 154 patients) with 138 matched primary tumors (PRIM) and synchronous or metachronous distant metastasis (MET) by immunohistochemistry, and mRNA sequencing in a subgroup of 20 matched pairs. Protein-based tumor cell subtypes and histomorphology remained stable during metastatic progression (concordance: 94%, 95% confidence interval [CI] 88-97%). In comparison, transcriptome-based molecular consensus subtypes exhibited higher heterogeneity between PRIM and MET (concordance: 45%, 95% CI 23-69%), with switches particularly occurring between luminal and stroma-rich tumors. Of note, all tumors classified as stroma rich showed luminal tumor cell differentiation. By an in-depth analysis, we found a negative correlation of luminal gene and protein expression with increasing desmoplastic stroma content, suggesting that luminal tumor cell differentiation of "stroma-rich tumors" is superimposed by gene expression signals stemming from the stromal compartment. Immunohistochemistry allows tumor cell subtyping into luminal, basal, or neuroendocrine classes that remain stable during metastatic progression. These findings expand our biological understanding of UC MET and have implications for future subtype-stratified clinical trials in patients with mUC. PATIENT SUMMARY: Urothelial carcinomas (UCs) occur in different appearances, the so-called molecular subtypes. These molecular subtypes will gain importance for the therapy of metastatic UCs in the future. We could demonstrate that the subtype remains stable during metastasis, which is highly relevant for future studies.

Heterogeneity of prostate-specific membrane antigen (PSMA) and PSMA-ligand uptake detection combining autoradiography and postoperative pathology in primary prostate cancer.

BACKGROUND: Targeting prostate-specific membrane antigen (PSMA) has been highly successful for imaging and treatment of prostate cancer. However, heterogeneity in immunohistochemistry indicates limitations in the effect of imaging and radionuclide therapy of multifocal disease. 99mTc-PSMA-I&S is a γ-emitting probe, which can be used for intraoperative lesion detection and postsurgical autoradiography (ARG). We aimed to study its intraprostatic distribution and compared it with (immuno)-histopathology. RESULTS: Seventeen patients who underwent RGS between 11/2018 and 01/2020 with a total of 4660 grids were included in the preliminary analysis. Marked intratumor and intra-patient heterogeneity of PSMA expression was detected, and PSMA negative foci were observed in all samples (100%). Heterogeneous intra-patient PSMA-ligand uptake was observed, and no significant correlation was present between the degree of heterogeneity of PSMA expression and PSMA-ligand uptake. Higher PSMA-ligand uptake was observed in GS ≥ 8 than GS < 8 (p < 0.001). The appearance of Gleason Pattern (GP) 4 was strongly associated with higher uptake (coefficient: 0.43, p < 0.001), while GP 5 also affected the uptake (coefficient: 0.07, p < 0.001). CONCLUSION: PSMA expression and PSMA-ligand uptake show marked heterogeneity. Prostate carcinoma with GP 4 showed significantly higher uptake compared with non-neoplastic prostate tissue. Our analyses extend the scope of applications of radiolabeled PSMA-ligands to ARG for identifying high-grade disease and using its signal as a noninvasive biomarker in prostate cancer.

Renal neuroendocrine tumors: clinical and molecular pathology with an emphasis on frequent association with ectopic Cushing syndrome.

Renal neuroendocrine tumors (RenNETs) are rare malignancies with largely unknown biology, hormone expression, and genetic abnormalities. This study aims to improve our understanding of the RenNETs with emphasis of functional, hormonal, and genetic features. Surgically resected RenNETs (N = 13) were retrieved, and immunohistochemistry and next-generation sequencing (NGS) were performed in all cases. In addition, all published RenNETs were systematically reviewed. Our cohort (4 men and 9 women, mean age 42, mean tumor size 7.6 cm) included 2 patients with Cushing syndrome (CS). WHO grade (23% G1, 54% G2, and 23% G3) and tumor progression did not correlate. CS-associated RenNETs (CS-RenNETs) showed a solid and eosinophilic histology and stained for ACTH, while the remaining non-functioning tumors had a trabecular pattern and expressed variably hormones somatostatin (91%), pancreatic polypeptide (63%), glucagon (54%), and serotonin (18%). The transcription factors ISL1 and SATB2 were expressed in all non-functioning, but not in CS-RenNETs. NGS revealed no pathogenic alterations or gene fusions. In the literature review (N = 194), 15 (8%) of the patients had hormonal syndromes, in which CS being the most frequent (7/15). Large tumor size and presence of metastasis were associated with shorter patients' survival (p < 0.01). RenNETs present as large tumors with metastases. CS-RenNETs differ through ACTH production and solid-eosinophilic histology from the non-functioning trabecular RenNETs that produce pancreas-related hormones and express ISL1 and SATB2. MEN1 or DAXX/ARTX abnormalities and fusion genes are not detected in RenNETs, indicating a distinct yet unknown molecular pathogenesis.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Membranous NECTIN-4 Expression Frequently Decreases during Metastatic Spread of Urothelial Carcinoma and Is Associated with Enfortumab Vedotin Resistance.

PURPOSE: The antibody-drug conjugate enfortumab vedotin (EV) releases a cytotoxic agent into tumor cells via binding to the membrane receptor NECTIN-4. EV was recently approved for patients with metastatic urothelial carcinoma (mUC) without prior assessment of the tumor receptor status as ubiquitous NECTIN-4 expression is assumed. Our objective was to determine the prevalence of membranous NECTIN-4 protein expression in primary tumors (PRIM) and patient-matched distant metastases (MET). EXPERIMENTAL DESIGN: Membranous NECTIN-4 protein expression was measured (H-score) by IHC in PRIM and corresponding MET (N = 137) and in a multicenter EV-treated cohort (N = 47). Progression-free survival (PFS) after initiation of EV treatment was assessed for the NECTIN-4-negative/weak (H-score 0-99) versus moderate/strong (H-score 100-300) subgroup. The specificity of the NECTIN-4 IHC staining protocol was validated by establishing CRISPR-Cas9-induced polyclonal NECTIN-4 knockouts. RESULTS: In our cohort, membranous NECTIN-4 expression significantly decreased during metastatic spread (Wilcoxon matched pairs P < 0.001; median H-score = 40; interquartile range, 0-140), with 39.4% of MET lacking membranous NECTIN-4 expression. In our multicenter EV cohort, absence or weak membranous NECTIN-4 expression (34.0% of the cohort) was associated with a significantly shortened PFS on EV (log-rank P < 0.001). CONCLUSIONS: Membranous NECTIN-4 expression is frequently decreased or absent in mUC tissue. Of note, the clinical benefit of EV strongly depends on membranous NECTIN-4 expression. Thus, our results are of highest clinical relevance and argue for a critical reconsideration of the current practice and suggest that the NECTIN-4 receptor status should be determined (ideally in a metastatic/progressive lesion) before initiation of EV. See related commentary by Aggen et al., p. 1377.

Spatial Immunephenotypes of Distant Metastases but not Matched Primary Urothelial Carcinomas Predict Response to Immune Checkpoint Inhibition.

BACKGROUND: The value of programmed cell death ligand-1 (PD-L1) to predict durable responses to immune checkpoint inhibitors (ICIs) in metastatic urothelial carcinoma (mUC) is inconsistent. We hypothesize that the use of archived primary tumor material (PRIM) for PD-L1 testing in clinical trials not properly reflecting the metastatic disease status (MET) contributes to this clinical issue. OBJECTIVE: To analyze the predictive and prognostic value of PD-L1, spatial immunephenotypes, and major histocompatibility complex class I (MHC-I) determined in patient-matched PRIM/MET. DESIGN, SETTING, AND PARTICIPANTS: PD-L1, spatial immunephenotypes, and MHC-I were examined in 154 mUC patients with at least one available pretreatment MET (138 patient-matched PRIM/MET pairs). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: PD-L1, spatial immunephenotype, and MHC-I status of (patient-matched PRIM and) pretreatment MET were correlated with chemotherapy and ICI response and outcomes. RESULTS AND LIMITATIONS: Discordance rates in patient-matched PRIM/MET were 25/30%, 36%, and 49% for PD-L1 (CPS10/IC5%), immunephenotypes, and MHC-I (loss vs preserved), respectively. Correlations with chemotherapy and ICI responses were observed for immunephenotypes and MHC-I status determined in MET (not for PD-L1 alone), but not in PRIM. In case of ICIs, patients with cytotoxic tumor immune microenvironment (TIME) showed durable responses with disease control rates of 90% and a hazard ratio for disease progression/death of 0.05 (95% confidence interval: 0.01-0.65) versus patients with immunedepleted MET (disease control rate 29%). MET MHC-I status added an incremental value to predict durable ICI responses. Limitations include the partly retrospective design and the lack of MET multisampling on individual patient level. CONCLUSIONS: The TIME is subject to substantial dynamics during metastatic evolution. MET immunephenotypes and MHC-I statuses show promising potential to predict chemotherapy and durable ICI responses, while the PRIM TIME does not. Thus, future clinical trials should rather rely on pretreatment MET biopsies reflecting the current immunological disease state than on PRIM. PATIENT SUMMARY: Prediction of chemotherapy and responses to immune checkpoint inhibitors might be possible using representative pretreatment metastatic biopsies.

NCALD as a potential predictive biomarker for the efficacy of platinum-based chemotherapy in ovarian cancer.

Aim: Since reliable response predictors to platinum-based chemotherapy in ovarian cancer (OC) are scarce, we characterize NCALD as a predictive biomarker. Materials & methods: NCALD mRNA (n = 100) and protein (n = 102) expression was analyzed in OC samples and associated with patient outcome. A stable OC cell line knockdown was generated and cellular response to platinum was explored. Results: High NCALD mRNA and protein expression was significantly associated with longer overall patient survival (p = 0.037/0.002). Knockdown experiments revealed a significant association between cisplatin sensitivity and NCALD expression. Conclusion: Low NCALD expression was associated with reduced sensitivity to platinum-based chemotherapy. NCALD may be a new biomarker candidate to identify patients who might benefit from platinum-based chemotherapy.

Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital.

Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians' satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.

Prevalence of Castleman's disease in patients suffering from cervical lymphadenopathy.

Objectives: To determine the prevalence of Castleman's disease (unicentric/idiopathic multicentric CD) in a retrospective cohort according to the newly defined international diagnostic criteria in patients, who underwent a lymph node removal at a tertiary care university hospital over a period of 10 years. Study design: Retrospective chart review. Material and methods: All Patients with cervical lymphadenopathy coded by ICD-10-CM with "I88.9," "R59.0," or "D47.Z2" between January 2010 and December 2020 and who underwent a lymph node extirpation were identified. In cases who met the diagnostic criteria for a potential unicentric or idiopathic multicentric CD (iMCD) diagnosis, the lymph node tissue was re-evaluated by a pathologist. Results: A total of 714 patients with cervical lymphadenopathy were included into this single-center retrospective study. After exclusion of patients with diseases that may mimic iMCD and cases for which material to perform histological re-evaluation was lacking, a subset of 75 patients with "nonspecific lymphadenitis" or "reactive hyperplasia of lymph node" was identified, who underwent a renewed histopathological examination. One case fulfilled both the major and minor criteria of an iMCD diagnosis, and further 15 cases matched the histological criterion of an iMCD diagnosis (one of the two major diagnostic criteria), so that a UCD diagnosis according to the new criteria could be accepted. Conclusion: In this cohort, the subsequent application of the new diagnostic criteria led to further cases of CD (1.9% compared to 0.1% before) being recognized. Although incidence and prevalence of UCD and iMCD are low, clinicians should keep in mind this differential diagnosis as effective therapies are available. Level of Evidence: 4.

MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases.

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine.

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Multicenter Evaluation of Tissue Classification by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree.

Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry.

Validation of 18F-rhPSMA-7 and 18F-rhPSMA-7.3 PET Imaging Results with Histopathology from Salvage Surgery in Patients with Biochemical Recurrence of Prostate Cancer.

18F-rhPSMA-7, and its single diastereoisomer form, 18F-rhPSMA-7.3, are prostate-specific membrane antigen (PSMA)-targeting radiopharmaceuticals. Here, we investigated their accuracy for the assessment of lymph node (LN) metastases validated by histopathology. Methods: Data from 58 patients with biochemical recurrence of prostate cancer after radical prostatectomy receiving salvage surgery after PET imaging with 18F-rhPSMA-7 or 18F-rhPSMA-7.3 were retrospectively reviewed. Two nuclear medicine physicians reviewed all PET scans and morphologic imaging in consensus. Readers were masked from the results of histopathology. PET and morphologic imaging were correlated with histopathology from resected LNs. Results: In 75 of 150 resected regions in 54 of 58 patients, tumor lesions were present in histopathology. The template-based specificity of PET (18F-rhPSMA-7 and 18F-rhPSMA-7.3 combined) and morphologic imaging was 93.3% and 100%, respectively. However, 18F-rhPSMA-7 and 18F-rhPSMA-7.3 PET detected metastases in 61 of 75 histopathologically proven metastatic LN fields (81.3%) whereas morphologic imaging was positive in only 9 of 75 (12.0%). The positive predictive value was 92.4% for 18F-rhPSMA-7 and 18F-rhPSMA-7.3 PET and 100% for morphologic imaging. 18F-rhPSMA-7 and 18F-rhPSMA-7.3 PET performance was significantly superior to morphologic imaging (difference in the areas under the receiver-operating-characteristic curves, 0.222; 95% CI, 0.147-0.298; P < 0.001). The mean size of PET-positive and histologically confirmed LN metastases was 6.3 ± 3.1 mm (range, 2-15 mm) compared with a mean size of 9.8 ± 2.5 mm (range, 7-15 mm) on morphologic imaging. Conclusion: 18F-rhPSMA-7 and 18F-rhPSMA-7.3 PET offer a high positive predictive value comparable to that reported for 68Ga-PSMA-11 and represent a valuable tool for guiding salvage lymphadenectomy.

Utility of 18F-rhPSMA-7.3 PET for Imaging of Primary Prostate Cancer and Preoperative Efficacy in N-Staging of Unfavorable Intermediate- to Very High-Risk Patients Validated by Histopathology.

18F-rhPSMA-7.3, the lead compound of a new class of radiohybrid prostate-specific membrane antigen (rhPSMA) ligand, is currently in phase III trials for prostate cancer (PCa) imaging. Here, we describe our experience in primary PCa staging. Methods: We retrospectively identified 279 patients with primary PCa who underwent 18F-rhPSMA-7.3 PET/CT (staging cohort). A subset of patients (83/279) subsequently underwent prostatectomy with lymph node (LN) dissection without prior treatment (efficacy cohort). The distribution of tumor lesions was determined for the staging cohort and stratified by National Comprehensive Cancer Network risk score. Involvement of pelvic LNs was assessed retrospectively by 3 masked independent central readers, and a majority rule was used for analysis. Standard surgical fields were rated on a 5-point scale independently for PET and for morphologic imaging. Results were compared with histopathologic findings on a patient, right-vs.-left, and template basis. Results: For the staging cohort, 18F-rhPSMA-7.3 PET was positive in 275 of 279 (98.6%), 106 of 279 (38.0%), 46 of 279 (16.5%), 65 of 279 (23.3%), and 5 of 279 (1.8%) patients for local, pelvic nodal, extrapelvic nodal, metastatic bone, and visceral metastatic disease, respectively. In the efficacy cohort, LN metastases were present in 24 of 83 patients (29%) and were located in 48 of 420 (11%) resected templates and in 33 of 166 (19.9%) hemipelvic templates in histopathology. The majority vote results showed that patient-level sensitivity, specificity, and accuracy for pelvic nodal metastases were 66.7% (95% CI, 44.7%-83.6%), 96.6% (95% CI, 87.3%-99.4%), and 88.0% (95% CI, 78.5%-93.8%), respectively, for 18F-rhPSMA-7.3 PET and 37.5% (95% CI, 19.6%-59.2%), 91.5% (95% CI, 80.6%-96.8%), and 75.9% (95% CI, 65.0%-84.3%), respectively, for morphologic imaging. 18F-rhPSMA-7.3 showed higher interobserver agreement than morphologic imaging (patient-level Fleiss κ = 0.54 [95% CI, 0.47-0.62] vs. 0.24 [95% CI, 0.17-0.31]). A mean SUV ratio of 6.6 (95% CI, 5.2-8.1) documented a high image contrast between local tumors and adjacent low urinary tracer retention. Conclusion: 18F-rhPSMA-7.3 PET offers diagnostic performance superior to morphologic imaging for primary N-staging of newly diagnosed PCa, shows lower interreader variation, and offers good distinction between primary-tumor activity and bladder background activity. With increasing National Comprehensive Cancer Network risk group, an increasing frequency of extraprostatic tumor lesions was observed.

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.

Immunocompromised Patients with Therapy-Refractory Chronic Skin Diseases Show Reactivation of Latent Epstein‒Barr Virus and Cytomegalovirus Infection.

Reactivation of latent Epstein‒Barr virus (EBV) and/or Cytomegalovirus (CMV) infection is a dreaded complication in immunocompromised patients receiving hematopoietic stem cell transplantation. Evidence is sparse on whether subclinical reactivation of viral infection may also be of clinical relevance in dermatological patients. We screened patients (N = 206) suffering from chronic skin diseases for subclinical reactivation of EBV and CMV infection. We found that immunocompromised patients with therapy-refractory chronic skin diseases showed higher rates of subclinical reactivation of CMV and EBV infection (6.7% vs. 0% for EBV and 16.7% vs. 5.6% for CMV) and a higher prevalence of virus-specific DNA in skin tissue (30.8% vs. 0% for EBV and 21.4% vs. 0% for CMV) than nonimmunocompromised patients with chronic skin diseases. T cells isolated from lesional skin exhibited up to 14-fold increased proliferation with production of T helper type 1 and T helper type 17 cytokines on stimulation with viral proteins, providing evidence for possible aggravation of the underlying skin diseases by viral infection. Improvement of skin lesions in patients with reactivation of CMV infection (n = 4) was observed on antiviral treatment. Our data suggest that subclinical reactivation of EBV and/or CMV infection is an under-recognized condition in the dermatological patient population with chronic skin diseases.

Radical Prostatectomy Without Prior Biopsy Following Multiparametric Magnetic Resonance Imaging and Prostate-specific Membrane Antigen Positron Emission Tomography.

A biopsy-free diagnostic pathway in prostate cancer (PC) is limited by the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI). The improved accuracy of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) raises the question whether this imaging modality can complement mpMRI to safely avoid biopsy prior to radical prostatectomy (RP). In this case series, we report the feasibility of primary RP without prior biopsy based on a high suspicion of significant PC in both mpMRI (Prostate Imaging Reporting and Data System [PI-RADS] score ≥4) and PSMA-PET (PET score ≥4 on a five-point Likert scale and maximum standardized uptake value ≥4.0) in 25 patients. All patients showed International Society of Urological Pathology (ISUP) grade ≥2 PC in postoperative histopathology. We report patient- and lesion-based comparisons with histopathology of the prostate specimen. Results of our case series may trigger the discussion about RP without prior biopsy as a possible option in well-selected patients. Our case series is limited by retrospective design and small sample size. We want to emphasize clearly that this practice should not be regarded as a standard procedure at the moment. Future studies with larger cohorts only inside a prospective, ethically approved study design are necessary to confirm these results.

The Impact of Histological Annotations for Accurate Tissue Classification Using Mass Spectrometry Imaging.

Knowing the precise location of analytes in the tissue has the potential to provide information about the organs' function and predict its behavior. It is especially powerful when used in diagnosis and prognosis prediction of pathologies, such as cancer. Spatial proteomics, in particular mass spectrometry imaging, together with machine learning approaches, has been proven to be a very helpful tool in answering some histopathology conundrums. To gain accurate information about the tissue, there is a need to build robust classification models. We have investigated the impact of histological annotation on the classification accuracy of different tumor tissues. Intrinsic tissue heterogeneity directly impacts the efficacy of the annotations, having a more pronounced effect on more heterogeneous tissues, as pancreatic ductal adenocarcinoma, where the impact is over 20% in accuracy. On the other hand, in more homogeneous samples, such as kidney tumors, histological annotations have a slenderer impact on the classification accuracy.