Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.

PURPOSE: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. METHODS: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. RESULTS: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). CONCLUSION: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.

Salivary excretion of systemically injected [18F]DCFPyL in prostate cancer patients undergoing PSMA scans.

Introduction: Prostate-specific membrane antigen (PSMA) is present in high amounts in salivary glands, but it is unclear whether labeled binders of PSMA are excreted in the saliva. Methods: Ten patients with prostate cancer underwent whole-body [18F]DCFPyL PET/CT (NCT03181867), and saliva samples were collected between 0-120 minutes post-injection. [18F]DCFPyL salivary excretion was measured over 120 minutes and expressed as %ID/g. Protein-associated binding was estimated by the percentage of [18F]DCFPyL versus parent radiotracer. Results: All PET scans of 10 patients (69 ± 8 years) with histologically confirmed prostate cancer (PSA= 2.4 ± 2.4, and Gleason Grade = 6-9) showed high uptake of [18F]-DCFPyL in salivary glands while 8 patients demonstrated high uptake in the saliva at 45 minutes. The intact [18F]-DCFPyL (98%) was also confirmed in the saliva samples at 120 min with increasing salivary radioactivity between 30-120 min. Conclusion: Systemically injected [18F]DCFPyL shows salivary gland uptake, an increasing amount of which is secreted in saliva over time and is not maximized by 120 minutes post-injection. Although probably insignificant for diagnostic studies, patients undergoing PSMA-targeted therapies should be aware of radioactivity in saliva.

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.

Near-infrared Photoimmunotherapy Targeting Cancer-Associated Fibroblasts in Patient-Derived Xenografts using a Humanized anti-Fibroblast Activation Protein Antibody.

Esophageal cancer remains a highly aggressive malignancy with a poor prognosis, despite ongoing advancements in treatments such as immunotherapy. The tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), plays a crucial role in driving the aggressiveness of esophageal cancer. In a previous study utilizing human-derived xenograft models, we successfully developed a novel cancer treatment that targeted CAFs with near-infrared photoimmunotherapy (NIR-PIT), as an adjuvant therapy. In this study, we sought to translate our findings toward clinical practice by employing patient-derived xenograft (PDX) models and utilizing humanized monoclonal antibodies, specifically Sibrotuzumab, which is anti-human fibroblast activation protein (FAP) antibody and already being investigated in clinical trials as monotherapy. PDX models derived from esophageal cancer patients were effectively established, preserving the expression of key biomarkers such as EGFR and FAP, as observed in primary tumors. The application of FAP-targeted NIR-PIT using Sibrotuzumab, conjugated with the photosensitizer IR700DX, exhibited precise binding and selective elimination of FAP-expressing fibroblasts in vitro. Notably, in our in vivo investigations using both cell line-derived xenograft and PDX models, FAP-targeted NIR-PIT led to significant inhibition of tumor progression compared to control groups, all without inducing adverse events such as weight loss. Immunohistological assessments revealed a substantial reduction in CAFs exclusively within the tumor microenvironment of both models, further supporting the efficacy of our approach. Thus, our study demonstrates the potential of CAF-targeted NIR-PIT employing Sibrotuzumab as a promising therapeutic avenue for clinical treatment of esophageal cancer patients.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.

Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18F-DCFPyL PSMA PET/CT With MRI.

PURPOSE: Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra. PATIENTS AND METHODS: Eighty-five patients who underwent both 18F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs. Fisher's exact tests and unpaired t tests were used to compare residual urine presence and prostatic urethra measurements between patients with and without midline radiotracer activity. The influence of anatomical factors including prostate volume and urethral curvature on urinary stagnation was also explored. RESULTS: Midline activity on PSMA PET imaging was seen in 14 patients included in the case group, whereas the remaining 71 with no midline activity constituted the control group. A total of 71.4% (10/14) and 29.6% (21/71) of patients in the case and control groups had urethral hyperintensity on T2W MRI, respectively (P < 0.01). Patients in the case group had significantly larger mean urethral dimensions, larger prostate volumes, and higher incidence of severe urethral curvature compared with the controls. CONCLUSIONS: Stagnated urine within the prostatic urethra is a potential confounding factor on PSMA PET scans. Integrating PET imaging with T2W MRI can mitigate false-positive calls, especially as PSMA PET/CT continues to gain traction in diagnosing localized prostate cancer.

The role of interventional radiology and molecular imaging for near infrared photoimmunotherapy.

Near infrared photoimmunotherapy (NIR-PIT) is a recently approved cancer therapy for recurrent head and neck cancer. It involves the intravenous administration of an antibody-photoabsorber (IRDye700DX: IR700) conjugate (APC) to target cancer cells, followed 24 h later by exposure to near infrared light to activate cell-specific cytotoxicity. NIR-PIT selectively targets cancer cells for destruction and activates a strong anticancer host immunity. The fluorescent signal emitted by IR700 enables the visualization of the APC in vivo using fluorescence imaging. Similarly, the activation of IR700 during therapy can be monitored by loss of fluorescence. NIR-PIT can be used with a variety of antibodies and therefore, a variety of cancer types. However, in most cases, NIR-PIT requires direct light exposure only achieved with interstitial diffuser light fibers that are placed with image-guided interventional needle insertion. In addition, the unique nature of NIR-PIT cell death, means that metabolic molecular imaging techniques such as PET and diffusion MRI can be used to assess therapeutic outcomes. This mini-review focuses on the potential implications of NIR-PIT for interventional radiology and therapeutic monitoring.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS: An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS: A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION: Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

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.

Restaging With Prostate-Specific Membrane Antigen Imaging in Metastatic Castration-Resistant Prostate Cancer: When Seeing More Is Detrimental to Care.

#PSMA is amazing new tech but is using it to expedite the call of disease progression helping #ProstateCancer patients?

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System.

Background and objective: Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI). Methods: A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa (κ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated. Key findings and limitations: Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied. Conclusions and clinical implications: The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance. Patient summary: Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

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.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

Phototruncation cell tracking with near-infrared photoimmunotherapy using heptamethine cyanine dye to visualise migratory dynamics of immune cells.

BACKGROUND: Noninvasive in vivo cell tracking is valuable in understanding the mechanisms that enhance anti-cancer immunity. We have recently developed a new method called phototruncation-assisted cell tracking (PACT), that uses photoconvertible cell tracking technology to detect in vivo cell migration. This method has the advantages of not requiring genetic engineering of cells and employing tissue-penetrant near-infrared light. METHODS: We applied PACT to monitor the migration of immune cells between a tumour and its tumour-draining lymph node (TDLN) after near-infrared photoimmunotherapy (NIR-PIT). FINDINGS: PACT showed a significant increase in the migration of dendritic cells (DCs) and macrophages from the tumour to the TDLN immediately after NIR-PIT. This migration by NIR-PIT was abrogated by inhibiting the sphingosine-1-phosphate pathway or Gαi signaling. These results were corroborated by intranodal immune cell profiles at two days post-treatment; NIR-PIT significantly induced DC maturation and increased and activated the CD8+ T cell population in the TDLN. Furthermore, PACT revealed that NIR-PIT significantly enhanced the migration of CD8+ T cells from the TDLN to the tumour four days post-treatment, which was consistent with the immunohistochemical assessment of tumour-infiltrating lymphocytes and tumour regression. INTERPRETATION: Immune cells dramatically migrated between the tumour and TDLN following NIR-PIT, indicating its potential as an immune-stimulating therapy. Also, PACT is potentially applicable to a wide range of immunological research. FUNDING: This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Centre for Cancer Research (grant number: ZIA BC011513 and ZIA BC011506).

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition.

Patients diagnosed with localized high-risk prostate cancer have higher rates of recurrence, and the introduction of neoadjuvant intensive hormonal therapies seeks to treat occult micrometastatic disease by their addition to definitive treatment. Sufficient profiling of baseline disease has remained a challenge in enabling the in-depth assessment of phenotypes associated with exceptional vs. poor pathologic responses after treatment. In this study, we report comprehensive and integrative gene expression profiling of 37 locally advanced prostate tumors prior to six months of androgen deprivation therapy (ADT) plus the androgen receptor (AR) inhibitor enzalutamide prior to radical prostatectomy. A robust transcriptional program associated with HER2 activity was positively associated with poor outcome and opposed AR activity, even after adjusting for common genomic alterations in prostate cancer including PTEN loss and expression of the TMPRSS2:ERG fusion. Patients experiencing exceptional pathologic responses demonstrated lower levels of HER2 and phospho-HER2 by immunohistochemistry of biopsy tissues. The inverse correlation of AR and HER2 activity was found to be a universal feature of all aggressive prostate tumors, validated by transcriptional profiling an external cohort of 121 patients and immunostaining of tumors from 84 additional patients. Importantly, the AR activity-low, HER2 activity-high cells that resist ADT are a pre-existing subset of cells that can be targeted by HER2 inhibition alone or in combination with enzalutamide. In summary, we show that prostate tumors adopt an AR activity-low prior to antiandrogen exposure that can be exploited by treatment with HER2 inhibitors.

Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively (P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses (P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18F-FDG and PSMA PET/CT scans.

Tumor Suppression by Anti-Fibroblast Activation Protein Near-Infrared Photoimmunotherapy Targeting Cancer-Associated Fibroblasts.

Cancer-associated fibroblasts (CAFs) constitute a prominent cellular component of the tumor stroma, with various pro-tumorigenic roles. Numerous attempts to target fibroblast activation protein (FAP), a highly expressed marker in immunosuppressive CAFs, have failed to demonstrate anti-tumor efficacy in human clinical trials. Near-infrared photoimmunotherapy (NIR-PIT) is a highly selective tumor therapy that utilizes an antibody-photo-absorbing conjugate activated by near-infrared light. In this study, we examined the therapeutic efficacy of CAF depletion by NIR-PIT in two mouse tumor models. Using CAF-rich syngeneic lung and spontaneous mammary tumors, NIR-PIT against FAP or podoplanin was performed. Anti-FAP NIR-PIT effectively depleted FAP+ CAFs, as well as FAP+ myeloid cells, and suppressed tumor growth, whereas anti-podoplanin NIR-PIT was ineffective. Interferon-gamma production by CD8 T and natural killer cells was induced within hours after anti-FAP NIR-PIT. Additionally, lung metastases were reduced in the treated spontaneous mammary cancer model. Depletion of FAP+ stromal as well as FAP+ myeloid cells effectively suppressed tumor growth in bone marrow chimeras, suggesting that the depletion of both cell types in one treatment is an effective therapeutic approach. These findings highlight a promising therapy for selectively eliminating immunosuppressive FAP+ cells within the tumor microenvironment.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

Near-infrared photoimmunotherapy targeting Nectin-4 in a preclinical model of bladder cancer.

Enfortumab vedotin (EV), an antibody-drug conjugate (ADC) that targets Nectin-4, has shown promising results in the treatment of bladder cancer. However, multiple resistance mechanisms that are unique to ADCs limit the therapeutic potential of EV in clinical practice. Here, we developed and tested a Nectin-4-targeted near-infrared photoimmunotherapy (NIR-PIT) that utilizes the same target as EV but utilizes a distinct cytotoxic and immunotherapeutic pathway in preclinical models of bladder cancer. NIR-PIT was effective in vitro against luminal subtype human bladder cancer cell lines (RT4, RT112, MGH-U3, SW780, and HT1376-luc), but not against other subtype cell lines (UMUC3 and T24). In vivo, the tumor site was clearly visible by Nectin-4-IR700 fluorescence 24 h after its administration, suggesting the potential as an intraoperative imaging modality. NIR-PIT significantly suppressed tumor growth and prolonged survival in SW780 and RT112 xenograft models. Weekly treatment with NIR-PIT further improved tumor control in RT112 xenograft models. The effectiveness of NIR-PIT was also confirmed in HT1376-luc orthotopic xenograft models. Histological analysis verified that NIR-PIT induced a significant pathologic response. Taken together, Nectin-4-targeted NIR-PIT shows promise as a treatment for luminal subtype bladder cancers.