Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds (P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

Muscle-invasive bladder cancer (MIBC) presents significant treatment challenges. Antibody-drug conjugates targeting human epidermal growth factor receptor 2 (HER2), trophoblast cell surface antigen 2 (TROP-2), and nectin cell adhesion molecule 4 (NECTIN4) offer promising therapeutic options. This study examined the spatial expression of HER2, TROP-2, and NECTIN4 in MIBC and metastases, their association with molecular subtypes, and clinical outcomes. Formalin-fixed, paraffin-embedded tissue samples from 251 patients with MIBC were analyzed using immunohistochemistry and tissue microarray analysis. Expression patterns between the tumor front and tumor center (TC) were compared, and statistical analyses assessed associations with molecular subtypes and clinical parameters. Additionally, 67 matched lymph node metastases and a secondary cohort comprising 16 distant metastases, including 7 matched primary tumors, were examined to explore the expression patterns in advanced tumor stages. In primary tumors, HER2 was predominantly negative (83%) but showed higher positivity in the TC. TROP-2 exhibited high overall expression (58% score 3+), whereas NECTIN4 displayed significant heterogeneity with stronger expression in the TC. Spatial overexpression of TROP-2 and NECTIN4 at the tumor front relative to the TC was associated with a better disease-free survival. Accurate assessment required 4 biopsies for HER2 and NECTIN4 and 3 for TROP-2. HER2 expression was associated with urothelial-like and genomically unstable molecular subtypes, whereas TROP-2 was widely expressed except in the mesenchymal-like subtype. NECTIN4 showed the absence of staining in basal, Mes-like, and Sc/NEC-like subtypes. Paired lymph node metastases showed higher expression scores for all 3 markers, whereas distant metastases showed reduced NECTIN4 expression. Additionally, lymph node metastases revealed a considerable heterogeneity for HER2 compared with their matched primary tumors. The spatial heterogeneity of HER2, TROP-2, and NECTIN4 expression necessitates multiple biopsies, particularly from the TC, for accurate evaluation. These findings underscore the need for personalized treatment strategies in MIBC, considering the increased risk of relapse associated with HER2 and NECTIN4 overexpression in the TC. Implementing a multibiopsy approach is critical to enhancing diagnostic accuracy.

The worldwide approval of the combination maintenance therapy of olaparib and bevacizumab in advanced high-grade serous ovarian cancer requires complex molecular diagnostic assays that are sufficiently robust for the routine detection of driver mutations in homologous recombination repair (HRR) genes and genomic instability (GI), employing formalin-fixed (FFPE) paraffin-embedded tumor samples without matched normal tissue. We therefore established a DNA-based hybrid capture NGS assay and an associated bioinformatic pipeline that fulfils our institution’s specific needs. The assay´s target regions cover the full exonic territory of relevant cancer-related genes and HRR genes and more than 20,000 evenly distributed single nucleotide polymorphism (SNP) loci to allow for the detection of genome-wide allele specific copy number alterations (CNA). To determine GI status, we implemented an %CNA score that is robust across a broad range of tumor cell content (25–85%) often found in routine FFPE samples. The assay was established using high-grade serous ovarian cancer samples for which BRCA1 and BRCA2 mutation status as well as Myriad MyChoice homologous repair deficiency (HRD) status was known. The NOGGO (Northeastern German Society for Gynecologic Oncology) GIS (GI-Score) v1 assay was clinically validated on more than 400 samples of the ENGOT PAOLA-1 clinical trial as part of the European Network for Gynaecological Oncological Trial groups (ENGOT) HRD European Initiative. The “NOGGO GIS v1 assay” performed using highly robust hazard ratios for progression-free survival (PFS) and overall survival (OS), as well a significantly lower dropout rate than the Myriad MyChoice clinical trial assay supporting the clinical utility of the assay. We also provide proof of a modular and scalable routine diagnostic method, that can be flexibly adapted and adjusted to meet future clinical needs, emerging biomarkers, and further tumor entities.

Extrachromosomal DNA (ecDNA) amplification enhances intercellular oncogene dosage variability and accelerates tumor evolution by violating foundational principles of genetic inheritance through its asymmetric mitotic segregation. Spotlighting high-risk neuroblastoma, we demonstrate how ecDNA amplification undermines the clinical efficacy of current therapies in cancers with extrachromosomal MYCN amplification. Integrating theoretical models of oncogene copy number–dependent fitness with single-cell ecDNA quantification and phenotype analyses, we reveal that ecDNA copy-number heterogeneity drives phenotypic diversity and determines treatment sensitivity through mechanisms unattainable by chromosomal oncogene amplification. We demonstrate that ecDNA copy number directly influences cell fate decisions in cancer cell lines, patient-derived xenografts, and primary neuroblastomas, illustrating how extrachromosomal oncogene dosage–driven phenotypic diversity offers a strong evolutionary advantage under therapeutic pressure. Furthermore, we identify senescent cells with reduced ecDNA copy numbers as a source of treatment resistance in neuroblastomas and outline a strategy for their targeted elimination to improve the treatment of MYCN-amplified cancers. Significance: ecDNA-driven tumor genome evolution provides a major challenge to curative cancer therapies. We demonstrate that ecDNA copy-number dynamics drives treatment resistance by promoting oncogene dosage–dependent phenotypic heterogeneity in MYCN-amplified cancers. Exploiting phenotype-specific vulnerabilities of ecDNA cells, therefore, presents a powerful strategy to overcome treatment resistance. See related commentary by Korsah, p. 1979

Immune checkpoint therapy (ICT) has shown promising potential in the treatment of multiple solid tumors. However, the role of ICT in pancreatic ductal adenocarcinoma (PDAC) remains limited. Patterns of immune checkpoints (ICs) in PDAC represent the basis for establishing a potent ICT. The aim of this study is to create a profile of IC expression and its prognostic relevance in cancer cells of PDAC. Therefore, tumor cells from peripheral and central tissue microarray (TMA) spots from histologically confirmed PDAC of 68 patients after tumor resection were investigated in terms of expressions of TIM3, IDO, B7H4, LAG3, VISTA, and PD-L1 using immunohistochemistry. The presence of the respective ICs was compared to overall survival (OS). The presence of VISTA and PD-L1 significantly correlates with shorter OS (median OS: 22 months vs. 7 months and 22 months vs. 11 months, respectively, p < 0.05). For the presence of TIM3, IDO, B7H4, and LAG3, no difference in OS was observed (p > 0.05). The analysis of OS of combined subgroups for VISTA and PD-L1 (VISTA and PD-L1 neg., VISTA pos. and PD-L1 neg., VISTA neg. and PD-L1 pos., and VISTA and PD-L1 pos.) yielded overall statistical significance difference (p = 0.02). These results suggest that the presence of VISTA and PD-L1 is of prognostic relevance and potentially qualifies them as targets for ICT.