Near-infrared photoimmunotherapy targeting PD-L1: Improved efficacy by preconditioning the tumor microenvironment.

Near-infrared photoimmunotherapy (NIR-PIT) is a new type of cancer therapy that employs antibody-IRDye700DX conjugates (AbPCs) and near-infrared (NIR) light at a wavelength of 689 nm, the excitation wavelength of IR700. Administered intravenously, injected AbPCs bind specifically to cells expressing the target antigen, whereupon NIR light exposure causes rapid, selective killing. This process induces an anticancer T cell response, leading to sustained anticancer host immune response. Programmed cell death ligand-1 (PD-L1) is a major inhibitory immune checkpoint molecule expressed in various cancers. In this study, we first assessed the efficacy of PD-L1-targeted NIR-PIT (αPD-L1-PIT) in immune-competent tumor mouse models. αPD-L1-PIT showed a significant therapeutic effect on the tumor models with high PD-L1 expression. Furthermore, αPD-L1-PIT induced an abscopal effect on distant tumors and long-term immunological memory. In contrast, αPD-L1-PIT was not as effective for tumor models with low PD-L1 expression. To improve the efficacy of PD-L1-targeted NIR-PIT, PEGylated interferon-gamma (IFNγ) was administered with αPD-L1-PIT. The combination therapy improved the treatment efficacy by increasing PD-L1 expression leading to more efficient cell killing by αPD-L1-PIT. Furthermore, the PEGylated IFNγ led to a CD8+ T cell-dominant tumor microenvironment (TME) with an enhanced anticancer T cell response after αPD-L1-PIT. As a result, even so-called cold tumors exhibited complete responses after αPD-L1-PIT. Thus, combination therapy of PEGylated IFNγ and PD-L1-targeted NIR-PIT has the potential to be an important future strategy for cancer immunotherapy.

Fibroblast activation protein-targeted near-infrared photoimmunotherapy depletes immunosuppressive cancer-associated fibroblasts and remodels local tumor immunity.

BACKGROUND: Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) play a critical role in tumor immunosuppression. However, targeted depletion of CAFs is difficult due to their diverse cells of origin and the resulting lack of specific surface markers. Near-infrared photoimmunotherapy (NIR-PIT) is a novel cancer treatment that leads to rapid cell membrane damage. METHODS: In this study, we used anti-mouse fibroblast activation protein (FAP) antibody to target FAP+ CAFs (FAP-targeted NIR-PIT) and investigated whether this therapy could suppress tumor progression and improve tumor immunity. RESULTS: FAP-targeted NIR-PIT induced specific cell death in CAFs without damaging adjacent normal cells. Furthermore, FAP-targeted NIR-PIT treated mice showed significant tumor regression in the CAF-rich tumor model accompanied by an increase in CD8+ tumor infiltrating lymphocytes (TILs). Moreover, treated tumors showed increased levels of IFN-γ, TNF-α, and IL-2 in CD8+ TILs compared with non-treated tumors, suggesting enhanced antitumor immunity. CONCLUSIONS: Cancers with FAP-positive CAFs in their TME grow rapidly and FAP-targeted NIR-PIT not only suppresses their growth but improves tumor immunosuppression. Thus, FAP-targeted NIR-PIT is a potential therapeutic strategy for selectively targeting the TME of CAF+ tumors.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma.

Osteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, a convolutional neural network was adversarially trained to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. Adversarial training improved domain adaption of a histologic subtype classifier from canines to humans, achieving an average multiclass F1 score of 0.77 (95% CI, 0.74-0.79) and 0.80 (95% CI, 0.78-0.81) when compared with the ground truth in canines and humans, respectively. Finally, this trained model, when used to characterize the histologic landscape of 306 canine OSs, uncovered distinct clusters with markedly different clinical responses to standard-of-care therapy.

Prostate cancer - major changes in the American Joint Committee on Cancer eighth edition cancer staging manual.

Answer questions and earn CME/CNE The eighth edition of the American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) Staging Manual has been updated and improved to ensure the highest degree of clinical relevance and to improve its utility for patient evaluation and clinical research. Major changes include: 1) pathologically organ-confined disease is now considered pT2 and is no longer subclassified by extent of involvement or laterality, 2) tumor grading now includes both the Gleason score (as in the seventh edition criteria) and the grade group (introduced in the eighth edition criteria), 3) prognostic stage group III includes select, organ-confined disease based on prostate-specific antigen and Gleason/grade group status, and 4) 2 statistical prediction models are included in the staging manual. The AJCC will continue to critically analyze emerging prostate cancer biomarkers and tools for their ability to prognosticate and guide treatment decision making with the highest level of accuracy and confidence for patients and physicians. CA Cancer J Clin 2017;67:245-253. © 2017 American Cancer Society.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy.

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

A Mathematical Model of TCR-T Cell Therapy for Cervical Cancer.

Engineered T cell receptor (TCR)-expressing T (TCR-T) cells are intended to drive strong anti-tumor responses upon recognition of the specific cancer antigen, resulting in rapid expansion in the number of TCR-T cells and enhanced cytotoxic functions, causing cancer cell death. However, although TCR-T cell therapy against cancers has shown promising results, it remains difficult to predict which patients will benefit from such therapy. We develop a mathematical model to identify mechanisms associated with an insufficient response in a mouse cancer model. We consider a dynamical system that follows the population of cancer cells, effector TCR-T cells, regulatory T cells (Tregs), and "non-cancer-killing" TCR-T cells. We demonstrate that the majority of TCR-T cells within the tumor are "non-cancer-killing" TCR-T cells, such as exhausted cells, which contribute little or no direct cytotoxicity in the tumor microenvironment (TME). We also establish two important factors influencing tumor regression: the reversal of the immunosuppressive TME following depletion of Tregs, and the increased number of effector TCR-T cells with antitumor activity. Using mathematical modeling, we show that certain parameters, such as increasing the cytotoxicity of effector TCR-T cells and modifying the number of TCR-T cells, play important roles in determining outcomes.

Near-infrared photoimmunotherapy in the models of hepatocellular carcinomas using cetuximab-IR700.

Epidermal growth factor receptor (EGFR) has emerged as an important therapeutic target in many cancers, and overexpression of EGFR is frequently observed in hepatocellular carcinomas (HCCs). Near-infrared photoimmunotherapy (NIR-PIT) is a new anticancer treatment that selectively damages the cell membrane of cancer cells after NIR light-induced photochemical reaction of IR700, which is bound to a targeting antibody on the cell membrane. NIR-PIT using cetuximab-IR700 has already been approved in Japan, is under review by the US Food and Drug Administration (FDA) for advanced head and neck cancers, and its safety has been established. However, EGFR has not been investigated as a target in NIR-PIT in HCCs. Here, we investigate the application of NIR-PIT using cetuximab-IR700 to HCCs using xenograft mouse models of EGFR-expressing HCC cell lines, Hep3B, HuH-7, and SNU-449. In vitro NIR-PIT using EGFR-targeted cetuximab-IR700 killed cells in a NIR light dose-dependent manner. In vivo NIR-PIT resulted in a delayed growth compared with untreated controls. In addition, in vivo NIR-PIT in both models showed histological signs of cancer cell damage, such as cytoplasmic vacuolation and nuclear dysmorphism. A significant decrease in Ki-67 positivity was also observed after NIR-PIT, indicating decreased cancer cell proliferation. This study suggests that NIR-PIT using cetuximab-IR700 has potential for the treatment of EGFR-expressing HCCs.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

BACKGROUND: Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden. METHODS: Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering. RESULTS: Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension. CONCLUSIONS: The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.

MRI-Targeted, Systematic, and Combined Biopsy for Prostate Cancer Diagnosis.

BACKGROUND: The use of 12-core systematic prostate biopsy is associated with diagnostic inaccuracy that contributes to both overdiagnosis and underdiagnosis of prostate cancer. Biopsies performed with magnetic resonance imaging (MRI) targeting may reduce the misclassification of prostate cancer in men with MRI-visible lesions. METHODS: Men with MRI-visible prostate lesions underwent both MRI-targeted and systematic biopsy. The primary outcome was cancer detection according to grade group (i.e., a clustering of Gleason grades). Grade group 1 refers to clinically insignificant disease; grade group 2 or higher, cancer with favorable intermediate risk or worse; and grade group 3 or higher, cancer with unfavorable intermediate risk or worse. Among the men who underwent subsequent radical prostatectomy, upgrading and downgrading of grade group from biopsy to whole-mount histopathological analysis of surgical specimens were recorded. Secondary outcomes were the detection of cancers of grade group 2 or higher and grade group 3 or higher, cancer detection stratified by previous biopsy status, and grade reclassification between biopsy and radical prostatectomy. RESULTS: A total of 2103 men underwent both biopsy methods; cancer was diagnosed in 1312 (62.4%) by a combination of the two methods (combined biopsy), and 404 (19.2%) underwent radical prostatectomy. Cancer detection rates on MRI-targeted biopsy were significantly lower than on systematic biopsy for grade group 1 cancers and significantly higher for grade groups 3 through 5 (P<0.01 for all comparisons). Combined biopsy led to cancer diagnoses in 208 more men (9.9%) than with either method alone and to upgrading to a higher grade group in 458 men (21.8%). However, if only MRI-target biopsies had been performed, 8.8% of clinically significant cancers (grade group ≥3) would have been misclassified. Among the 404 men who underwent subsequent radical prostatectomy, combined biopsy was associated with the fewest upgrades to grade group 3 or higher on histopathological analysis of surgical specimens (3.5%), as compared with MRI-targeted biopsy (8.7%) and systematic biopsy (16.8%). CONCLUSIONS: Among patients with MRI-visible lesions, combined biopsy led to more detection of all prostate cancers. However, MRI-targeted biopsy alone underestimated the histologic grade of some tumors. After radical prostatectomy, upgrades to grade group 3 or higher on histopathological analysis were substantially lower after combined biopsy. (Funded by the National Institutes of Health and others; Trio Study ClinicalTrials.gov number, NCT00102544.).

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers.

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates.

BACKGROUND: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis. PURPOSE: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm. STUDY TYPE: Retrospective. SUBJECTS: 615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy. FIELD STRENGTH/SEQUENCE: 3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced. ASSESSMENTS: Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category. STATISTICAL TESTS: Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant. RESULTS: 385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09). DATA CONCLUSION: Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer.

Imaging has traditionally played a minor role in the diagnosis and staging of prostate cancer. However, recent controversies generated by the use of prostate-specific antigen (PSA) screening followed by random biopsy have encouraged the development of new imaging methods for prostate cancer. Multiparametric magnetic resonance imaging (mpMRI) has emerged as the imaging method best able to detect clinically significant prostate cancers and to guide biopsies. Here, the authors explain what mpMRI is and how it is used clinically, especially with regard to high-risk populations, and we discuss the impact of mpMRI on treatment decisions for men with prostate cancer. CA Cancer J Clin 2016;66:326-336. © 2015 American Cancer Society.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy.

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging.

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.

Intercellular adhesion molecule-1-targeted near-infrared photoimmunotherapy of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and conventional chemotherapy and molecular-targeted therapies show limited efficacy. Near-infrared photoimmunotherapy (NIR-PIT) is a new anticancer treatment that selectively damages the cell membrane of cancer cells based on NIR light-induced photochemical reactions of the antibody (Ab)-photoabsorber (IRDye700Dx) conjugate and the cell membrane. TNBC is known to express several adhesion molecules on the cell surface providing a potential new target for therapy. Here, we investigated the therapeutic efficacy of intercellular adhesion molecule-1 (ICAM-1)-targeted NIR-PIT using xenograft mouse models subcutaneously inoculated with two human ICAM-1-expressing TNBC cell lines, MDAMB468-luc and MDAMB231 cells. In vitro ICAM-1-targeted NIR-PIT damaged both cell types in a NIR light dose-dependent manner. In vivo ICAM-1-targeted NIR-PIT in both models showed early histological signs of cancer cell damage, such as cytoplasmic vacuolation. Even among the cancer cells that appeared to be morphologically intact within 2 h post treatment, abnormal distribution of the actin cytoskeleton and a significant decrease in Ki-67 positivity were observed, indicating widespread cellular injury reflected in cytoplasmic degeneration. Such damage to cancer cells by NIR-PIT significantly inhibited subsequent tumor growth and improved survival. This study suggests that ICAM-1-targeted NIR-PIT could have potential clinical application in the treatment of TNBC.

Opening up new VISTAs: V-domain immunoglobulin suppressor of T cell activation (VISTA) targeted near-infrared photoimmunotherapy (NIR-PIT) for enhancing host immunity against cancers.

V-domain immunoglobulin suppressor of T cell activation (VISTA) is an inhibitory immune checkpoint molecule that is broadly expressed on lymphoid and myeloid cells, including regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs). Near-infrared photoimmunotherapy (NIR-PIT) is a cancer treatment that utilizes an antibody-photoabsorber (IRDye 700DX NHS ester) conjugate to selectively kill target cells after the local application of NIR light. Depletion of VISTA-expressing cells in the tumor microenvironment (TME) using NIR-PIT could enhance anti-tumor immune responses by removing immune suppressive cells. The purpose of this study was to evaluate the anti-tumor efficacy of VISTA-targeted NIR-PIT using two murine tumor models, MC38-luc and LL2-luc. VISTA was expressed on T cells including Tregs and MDSCs in the TME of these tumors. In contrast, CD45 - cells, including cancer cells, did not express VISTA. VISTA-targeted NIR-PIT depleted VISTA-expressing cells ex vivo. In vivo VISTA-targeted NIR-PIT inhibited tumor progression and prolonged survival in both models. After VISTA-targeted NIR-PIT, augmented CD8 + T cell and dendritic cell activation were observed in regional lymph nodes. In conclusion, VISTA-targeted NIR-PIT can effectively treat tumors by decreasing VISTA-expressing immune suppressor cells in the TME. Local depletion of VISTA-expressing cells in the tumor bed using NIR-PIT is a promising new cancer immunotherapy for treating various types of tumors.

Selection of antibody and light exposure regimens alters therapeutic effects of EGFR-targeted near-infrared photoimmunotherapy.

Near-infrared photoimmunotherapy (NIR-PIT) is a cell-specific cancer therapy that uses an antibody-photoabsorber (IRDye700DX, IR700) conjugate (APC) and NIR light. Intravenously injected APC binds the target cells, and subsequent NIR light exposure induces immunogenic cell death only in targeted cells. Panitumumab and cetuximab are antibodies that target human epidermal growth factor receptor (hEGFR) and are suitable for NIR-PIT. In athymic nude mouse models, panitumumab-based NIR-PIT showed superior therapeutic efficacy compared to cetuximab-based NIR-PIT because of the longer half-life of panitumumab-IR700 (pan-IR700) compared with cetuximab-IR700 (cet-IR700). Two light exposures on two consecutive days have also been shown to induce superior effects compared to a single light exposure in the athymic nude mouse model. However, the optimal regimen has not been assessed in immunocompetent mice. In this study, we compared panitumumab and cetuximab in APCs for NIR-PIT, and single and double light exposures using a newly established hEGFR-expressing cancer cell line derived from immunocompetent C57BL/6 mice (mEERL-hEGFR cell line). Fluorescence imaging showed that the decline of pan-IR700 was slower than cet-IR700 confirming a longer clearance time. Among all the combinations tested, mice receiving pan-IR700 and double light exposure showed the greatest tumor growth inhibition. This group was also shown to activate CD8+ T lymphocytes in lymph nodes and accumulate CD8+ T lymphocytes to a greater extent within the tumor compared with the control group. These results showed that APCs with longer half-life and double light exposure lead to superior outcomes in cancer cell-targeted NIR-PIT in an immunocompetent mouse model.

Near-infrared photoimmunotherapy induced tumor cell death enhances tumor dendritic cell migration.

Near-infrared photoimmunotherapy (NIR-PIT) selectively kills tumor cells to which the photo-absorber dye IR700DX-conjugated antibodies are bound and induces a systemic anti-tumor immune response. NIR-PIT induces immunogenic cell death (ICD), releases damage-associated molecular patterns (DAMPs) molecules from dying tumor cells, and activates dendritic cells (DCs). However, it is unclear whether NIR-PIT affects migration of tumor-infiltrating (Ti)-DCs to draining lymph nodes (dLNs), where a systemic anti-tumor response is induced. Here, we utilized in vivo photolabeling of Ti-DCs in tumors in photoconvertible protein Kikume Green-Red (KikGR) mice to show that NIR-PIT enhanced migration of Ti-DCs including cDC1s, cDC2s, and CD326+ DCs to dLNs. This effect was abolished by blocking adenosine triphosphate (ATP), one of the DAMPs molecules, as well as by inhibition of Gαi signaling by pertussis toxin. Thus, ICD induction by NIR-PIT stimulates Ti-DC migration to dLNs via ATP-P2X7 receptor and Gαi protein-coupled receptor signaling pathways and may augment tumor antigen presentation to induce anti-tumor T cells in dLNs.