Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study.

BACKGROUND: The diagnosis of cardiac amyloidosis can be established non-invasively by scintigraphy using bone-avid tracers, but visual assessment is subjective and can lead to misdiagnosis. We aimed to develop and validate an artificial intelligence (AI) system for standardised and reliable screening of cardiac amyloidosis-suggestive uptake and assess its prognostic value, using a multinational database of 99mTc-scintigraphy data across multiple tracers and scanners. METHODS: In this retrospective, international, multicentre, cross-tracer development and validation study, 16 241 patients with 19 401 scans were included from nine centres: one hospital in Austria (consecutive recruitment Jan 4, 2010, to Aug 19, 2020), five hospital sites in London, UK (consecutive recruitment Oct 1, 2014, to Sept 29, 2022), two centres in China (selected scans from Jan 1, 2021, to Oct 31, 2022), and one centre in Italy (selected scans from Jan 1, 2011, to May 23, 2023). The dataset included all patients referred to whole-body 99mTc-scintigraphy with an anterior view and all 99mTc-labelled tracers currently used to identify cardiac amyloidosis-suggestive uptake. Exclusion criteria were image acquisition at less than 2 h (99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid, 99mTc-hydroxymethylene diphosphonate, and 99mTc-methylene diphosphonate) or less than 1 h (99mTc-pyrophosphate) after tracer injection and if patients' imaging and clinical data could not be linked. Ground truth annotation was derived from centralised core-lab consensus reading of at least three independent experts (CN, TT-W, and JN). An AI system for detection of cardiac amyloidosis-associated high-grade cardiac tracer uptake was developed using data from one centre (Austria) and independently validated in the remaining centres. A multicase, multireader study and a medical algorithmic audit were conducted to assess clinician performance compared with AI and to evaluate and correct failure modes. The system's prognostic value in predicting mortality was tested in the consecutively recruited cohorts using cox proportional hazards models for each cohort individually and for the combined cohorts. FINDINGS: The prevalence of cases positive for cardiac amyloidosis-suggestive uptake was 142 (2%) of 9176 patients in the Austrian, 125 (2%) of 6763 patients in the UK, 63 (62%) of 102 patients in the Chinese, and 103 (52%) of 200 patients in the Italian cohorts. In the Austrian cohort, cross-validation performance showed an area under the curve (AUC) of 1·000 (95% CI 1·000-1·000). Independent validation yielded AUCs of 0·997 (0·993-0·999) for the UK, 0·925 (0·871-0·971) for the Chinese, and 1·000 (0·999-1·000) for the Italian cohorts. In the multicase multireader study, five physicians disagreed in 22 (11%) of 200 cases (Fleiss' kappa 0·89), with a mean AUC of 0·946 (95% CI 0·924-0·967), which was inferior to AI (AUC 0·997 [0·991-1·000], p=0·0040). The medical algorithmic audit demonstrated the system's robustness across demographic factors, tracers, scanners, and centres. The AI's predictions were independently prognostic for overall mortality (adjusted hazard ratio 1·44 [95% CI 1·19-1·74], p<0·0001). INTERPRETATION: AI-based screening of cardiac amyloidosis-suggestive uptake in patients undergoing scintigraphy was reliable, eliminated inter-rater variability, and portended prognostic value, with potential implications for identification, referral, and management pathways. FUNDING: Pfizer.

Examining the Relationship and Prognostic Significance of Cell-Free DNA Levels and the PSMA-Positive Tumor Volume in Men with Prostate Cancer: A Retrospective-Prospective [68Ga]Ga-PSMA-11 PET/CT Study.

Functional imaging with prostate-specific membrane antigen (PSMA) ligands has emerged as the standard imaging method for prostate cancer (PCA). In parallel, the analysis of blood-derived, cell-free DNA (cfDNA) has been shown to be a promising quantitative biomarker of PCA aggressiveness and patient outcome. This study aimed to evaluate the relationship and prognostic value of cfDNA concentrations and the PSMA-positive tumor volume (PSMA-TV) in men with PCA undergoing [68Ga]Ga-PSMA-11 PET/CT imaging. Methods: We recruited 148 men with histologically proven PCA (mean age, 70.7 ± 7.7 y) who underwent [68Ga]Ga-PSMA-11 PET/CT (184.9 ± 18.9 MBq) and blood sampling between March 2019 and August 2021. Among these, 74 (50.0%) had hormone-sensitive PCA and 74 (50.0%) had castration-resistant PCA (CRPC). All patients provided written informed consent before blood sample collection and imaging. The cfDNA was extracted and quantified, and PSMA-expressing tumor lesions were delineated to extract the PSMA-TVs. The Spearman coefficient assessed correlations between PSMA-TV and cfDNA concentrations and cfDNA's relation with clinical parameters. The Kruskal-Wallis test examined the mean cfDNA concentration differences based on PSMA-TV quartiles for significantly correlated patient groups. Log-rank and multivariate Cox regression analyses evaluated the prognostic significance of high and low cfDNA and PSMA-TV levels for overall survival. Results: Weak positive correlations were found between cfDNA concentration and PSMA-TV in the overall group (r = 0.16, P = 0.049) and the CRPC group (r = 0.31, P = 0.007) but not in hormone-sensitive PCA patients (r = -0.024, P = 0.837). In the CRPC cohort, cfDNA concentrations significantly differed between PSMA-TV quartiles 4 and 1 (P = 0.002) and between quartiles 4 and 2 (P = 0.016). Survival outcomes were associated with PSMA-TV (P < 0.0001, P = 0.004) but not cfDNA (P = 0.174, P = 0.12), as per the log-rank and Cox regression analysis. Conclusion: These findings suggest that cfDNA might serve as a biomarker of advanced, aggressive CRPC but does not reliably reflect total tumor burden or prognosis. In comparison, [68Ga]Ga-PSMA-11 PET/CT provides a highly granular and prognostic assessment of tumor burden across the spectrum of PCA disease progression.

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters.

Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NNs challenging to apply in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.

Antihormonal-Treatment Status Affects 68Ga-PSMA-HBED-CC PET Biodistribution in Patients with Prostate Cancer.

Androgen deprivation therapy (ADT) is known to influence the prostate-specific membrane antigen (PSMA) expression of prostate cancer, potentially complicating the interpretation of PSMA ligand PET findings and affecting PSMA radioligand therapy. However, the impact of ADT on PSMA ligand biodistribution in nontumorous organs is not well understood. Methods: Men (n = 112) with histologically proven prostate cancer who underwent 68Ga-PSMA-HBED-CC (68Ga-PSMA-11) PET/CT between November 2015 and July 2021 at the Medical University Vienna with known ADT status were retrospectively recruited. Fifty-six patients were on gonadotropin-releasing hormone-interfering ADT at the time of imaging (ADT group), whereas 56 patients with no history of ADT served as a control group. Physiologically PSMA-expressing organs (salivary glands, kidneys, liver, and spleen) were delineated, and their uptake was compared according to their data distributions. Multivariate regression analysis assessed the relationship between renal, hepatic, splenic, and salivary gland uptake and the explanatory variables metabolic tumor volume, glomerular filtration rate, and ADT status. Results: ADT was associated with lower levels of PSMA uptake in the kidneys (SUVmean: Δ[ADT - control] = -7.89; 95% CI, -10.73 to -5.04; P < 0.001), liver (SUVpeak: Δ[ADT - control] = -2.3; 95% CI, -5.72 to -0.93; P = 0.003), spleen (SUVpeak: Δ[ADT - control] = -1.27; 95% CI, -3.61 to -0.16; P = 0.033), and salivary glands (SUVmean: Δ[ADT - control] = -1.04; 95% CI, -2.48 to -0.13; P = 0.027). In a multivariate analysis, ADT was found to be associated with lower renal (SUVmean: ß = -7.95; 95% CI, -11.06 to -4.84; P < 0.0001), hepatic (SUVpeak: ß = -7.85; 95% CI, -11.78 to -3.91; P < 0.0001), splenic (SUVpeak: ß = -5.83; 95% CI, -9.95 to -1.7; P = 0.006), and salivary gland (SUVmean: ß = -1.47; 95% CI, -2.76 to -0.17; P = 0.027) uptake. A higher glomerular filtration rate was associated with a higher renal SUVmean (ß = 0.16; 95% CI, 0.05 to 0.26; P = 0.0034). Conclusion: These findings suggest that ADT systemically modulates PSMA expression, which may have implications for treatment-optimizing and side-effect-minimizing strategies for PSMA radioligand therapies, particularly those using more potent 225Ac-labeled PSMA conjugates.

Expression of immunoproteasome genes is regulated by cell-intrinsic and -extrinsic factors in human cancers.

Based on transcriptomic analyses of thousands of samples from The Cancer Genome Atlas, we report that expression of constitutive proteasome (CP) genes (PSMB5, PSMB6, PSMB7) and immunoproteasome (IP) genes (PSMB8, PSMB9, PSMB10) is increased in most cancer types. In breast cancer, expression of IP genes was determined by the abundance of tumor infiltrating lymphocytes and high expression of IP genes was associated with longer survival. In contrast, IP upregulation in acute myeloid leukemia (AML) was a cell-intrinsic feature that was not associated with longer survival. Expression of IP genes in AML was IFN-independent, correlated with the methylation status of IP genes, and was particularly high in AML with an M5 phenotype and/or MLL rearrangement. Notably, PSMB8 inhibition led to accumulation of polyubiquitinated proteins and cell death in IPhigh but not IPlow AML cells. Co-clustering analysis revealed that genes correlated with IP subunits in non-M5 AMLs were primarily implicated in immune processes. However, in M5 AML, IP genes were primarily co-regulated with genes involved in cell metabolism and proliferation, mitochondrial activity and stress responses. We conclude that M5 AML cells can upregulate IP genes in a cell-intrinsic manner in order to resist cell stress.

Meeting report--9th IRIC International Symposium on Molecular Targets in Cancer Genomics.

Graduate students and postdoctoral fellows at the Institute for Research in Immunology and Cancer (IRIC) organized the 9th IRIC International Symposium on 14-15 May, 2015. The symposium was held at the IRIC, an ultra-modern research hub and training center located on the hilltop of the Université de Montréal campus in Montreal, Canada. This year's title was 'Molecular Targets in Cancer Genomics', reflecting the common interest of the IRIC student community. Through four broadly themed sessions, organizers sought to highlight the new generation of anti-cancer strategies including targeted therapies directed against actionable cancer-specific mutations, and immunotherapies, which enhance immune responses against cancer. Both targeted and immunotherapies are tailored to cancer-specific features, and require precise knowledge of cancer cells, from their genome to their proteome. The focus of this symposium was on translating the molecular basis of cancer into a functional understanding of aberrant pathways, and to uncover novel targets to be exploited for cancer therapeutic strategies.