Citations

Abstract Aims Lipoprotein(a) [Lp(a)] is a causal risk factor for cardiovascular events. However, the effect of Lp(a) on coronary plaque composition and high-risk plaque features has not been fully characterized. This study aimed to investigate the association between Lp(a) and coronary atherosclerotic plaque phenotype at the plaque level. Methods and results This study included 710 patients who underwent coronary computed tomography angiography (CCTA) and had Lp(a) measured between 2008 and 2024. CCTA scans were analysed with a previously validated artificial intelligence-based algorithm (AI-QCT, Cleerly Inc.). The association of Lp(a) with non-calcified and calcified plaque volumes and HRP features was evaluated at the plaque level using generalized estimating equation models adjusted for traditional cardiovascular risk factors. Among the 710 patients, the mean age was 56 ± 10 years and 379 (53%) were male. In total, 3642 plaques were identified. In the adjusted plaque-level analysis, elevated Lp(a) (≥150 nmol/L) was associated with an increase in non-calcified plaque volume per plaque (P = 0.009), but not with an increase in calcified plaque volume (P = 0.81). Furthermore, elevated Lp(a) (≥150 nmol/L) was strongly associated with the presence of high-risk plaque at the plaque level (adjusted odds ratio: 1.78, 95% CI: 1.25–2.54, P = 0.001), whereas low-density lipoprotein-cholesterol and high-sensitivity C-reactive protein were not (P > 0.05). Conclusion In this plaque-level CCTA study of 3642 individual plaques, elevated Lp(a) levels were independently associated with increased non-calcified plaque volume and with the presence of high-risk plaque, but not with calcified plaque volume. These findings elucidate the impact of Lp(a) on unstable plaque phenotypes, even in patients without prior cardiovascular events.

Abstract Background Computed tomography coronary angiography (CCTA) has emerged as a powerful non-invasive imaging modality for the evaluation of coronary artery disease (CAD), with a global trend towards a "CT-first" strategy in stable patients with suspected CAD. While overall beneficial, this approach requires a shift in resources, straining specific parts of healthcare systems. Pre-test probability (PTP) models stratify risks of obstructive CAD and are intended to result in better utilization of CCTA. However, these models show variability across subpopulations and have modest discriminatory power with low positive predictive values (PPV). Purpose This study aims to leverage explainable artificial intelligence (XAI) for optimization of PTP models. Methods Patients that underwent a CCTA for suspected CAD with a CAD-Reporting and Data System (CAD-RADS) classification and Coronary Artery Calcium Score (CACS) were included. Obstructive CAD was defined as stenosis >50% (CAD-RADS 3 or higher). Missing data was imputed. The Risk Factor Weighted Clinical Likelihood (RF-CL) model based on clinical risk factors (age, sex, type of symptoms, smoking, hypertension, dyslipidemia, diabetes and familial predisposition) was calculated. Two gradient descent-based decision tree models, "AI-ECG" and "AI-CAC" were developed. AI-ECG is built using clinical risk factors and electrocardiogram (ECG) classification. AI-CAC is built using the aforementioned features with addition of CACS. Sensitivity analysis was performed using a 15% threshold, aligning with the threshold for CCTA acquisition. Comparative analysis was performed using Python 3.10.2, with area under the curve (AUC) analysis using the DeLong test and feature importance calculation using shapely values. Preliminary results: We identified 6178 patients with a CCTA between 2022 and 2024 in the Cardiology Centers Netherlands (CCN) and included 2083 patients (mean age 57.4 ± 10.2; 48.1% male). Obstructive CAD was present in 413 (19.8%) patients. The RF-CL model was outperformed by the AI-ECG model (AUC: 0.69 vs 0.80, p<0.001) and the AI-CACS model (AUC: 0.69 vs 0.90, p<0.001). Although sensitivity increased in both the AI-ECG and AI-CACS models from 0.46 to 0.87 and 0.83 respectively, PPV increased only in the AI-CACS model (0.37 to 0.61). Most important features were CACS, age, sex, ECG classification and familial predisposition. Conclusion and Discussion Our study demonstrates that the incorporation of XAI with clinical risk factors, ECG classification and CACS improves the pre-test probability estimation of obstructive CAD. ECG classification appears to be a robust feature. Adopting these models can stratify patients at risk for obstructive CAD, enabling targeted use of CCTA, reducing unnecessary testing, and thus alleviate the burden on healthcare systems. However, further external validation is necessary to assess the model’s performance in prospective real-world settings.Sensitivity analysis feature importance

Abstract Background Coronary Computed Tomography Angiography (CCTA) is the first-line diagnostic tool for suspected stable Coronary Artery Disease (CAD) to rule out obstructive CAD and is recommended for risk stratification. Both obstructive CAD and total plaque burden on CCTA predict cardiovascular (CV) risk. Prior studies demonstrated that type 2 diabetes (T2D) patients exhibit greater plaque burden and higher CV event risk. Purpose This study evaluates whether T2D patients undergoing CCTA for stable CAD symptoms, when matched for traditional cardiovascular risk factors, have a higher prevalence of CAD and greater atherosclerotic plaque burden. Methods A total of 568 patients were included: 284 T2D patients were 1:1 propensity-matched to 284 non-T2D patients based on age, sex, and CV risk factors. All underwent diagnostic CCTA between January 2022 and November 2023. CCTA parameters included CAD-Reporting and Data System (RADS) classification, segment involvement score (SIS), coronary artery calcium (CAC) score, and plaque composition. SIS was defined as the number of segments with at least mild plaque. Coronary segmentation followed the 16-segment model (SCCT). Plaque burden was classified as P1 (mild), P2 (moderate), P3 (severe), and P4 (extensive) per CAD-RADS 2.0, based on SIS, CAC score, and the number and severity of affected vessels. Results The mean age was 60.8 ± 9.97 years (T2D) vs. 61.2 ± 9.80 years (non-T2D), with similar gender distribution (48.6% vs. 48.9% female). BMI was significantly higher in T2D patients (29.2 vs. 26.5, p < 0.05). Smoking was less frequent in T2D patients (56.0% vs. 60.9%, p = 0.03). Other CV risk factors were similar. T2D patients were more likely to use antiplatelet and lipid-lowering agents. CAD prevalence was higher in T2D patients (83.1% vs. 76.4%, p < 0.05), while obstructive CAD was not significantly higher in T2D (33.5% vs. 27.5%, p = 0.13). No significant differences were observed in 1-, 2-, or 3-vessel disease. The SIS was significantly higher in T2D patients (3.85 vs. 3.37, p = 0.03). CAC scores were higher in T2D patients (median 60 [6–369] vs. 31 [0–224]), though not statistically significant. For plaque burden classification, P2 (moderate plaque burden) was more common in non-T2D patients (23.6% vs. 15.5%, p = 0.02), while P4 (extensive plaque burden) was significantly higher in T2D patients (21.1% vs. 14.1%, p = 0.03). Conclusion When matched for cardiovascular risk factors, T2D patients more frequently exhibited CAD on CCTA, with higher SIS scores and a greater prevalence of extensive plaque burden (P4 classification). Despite greater use of preventive medication and a lower smoking prevalence in the T2D group, plaque burden remained higher in these patients. These findings emphasize the need for improved risk stratification and preventive strategies in individuals with T2D.

Abstract Background Coronary computed tomography angiography (CCTA) is a non-invasive diagnostic modality for assessing coronary artery disease (CAD), with a growing global trend favouring a "CT-first" approach. However, this strategy strains healthcare systems as demand for CCTA increases. Pre-test probability (PTP) models for obstructive CAD are used to optimize the usage of CCTA. However, these models perform inconsistently across different subpopulations and offer limited discriminatory accuracy, with low positive predictive values (PPV). This study aims to enhance the PTP models with explainable artificial intelligence (XAI) on electrocardiogram (ECG) data. Methods Patients who underwent CCTA for suspected CAD with both CAD-Reporting and Data System (CAD-RADS) classification and Coronary Artery Calcium Score (CACS) were included. Obstructive CAD was defined as CAD-RADS 3 or above. Missing values were imputed. The Risk Factor Weighted Clinical Likelihood (RF-CL) model, based on clinical variables (age, sex, symptom type, smoking, hypertension, dyslipidaemia, diabetes, and family history) was computed. Two gradient descent decision tree models, "AI-ECG" and "AI-CAC," were developed for the pilot study. AI-ECG used clinical risk factors and ECG classification as abnormal, borderline or normal, while AI-CAC further incorporated CACS. We used a cutoff value of 15%, reflecting current guidelines. Comparative analysis was performed using area under the curve (AUC) analysis, the DeLong test for statistical comparison, and feature importance using SHAP values. We intend to develop and present a model based on ECG signal data during the conference. Pilot results We identified 6 178 patients who underwent CCTA between 2022 and 2024 at the Cardiology Centers Netherlands (CCN), of which 2 083 were included (mean age 57.4 ± 10.2 years; 48.1% male). Obstructive CAD was found in 413 (19.8%) patients. The RF-CL model was outperformed by both AI-ECG (AUC: 0.69 vs. 0.80, p<0.001) and AI-CACS (AUC: 0.69 vs. 0.90, p<0.001). Sensitivity increased in both AI models, from 0.46 to 0.87 for AI-ECG and to 0.83 for AI-CACS, while PPV improved only in the AI-CACS model (from 0.37 to 0.61). Key predictive features were CACS, age, sex, ECG classification, and family history. Conclusion and Discussion Our pilot study shows that integrating XAI with clinical risk factors and ECG classification enhances PTP estimation for obstructive CAD. Implementing this model can better stratify patients at risk of obstructive CAD, support more focused CCTA use and reduce unnecessary testing. Nonetheless, further external validation is needed to assess model performance in real-world, prospective clinical settings. Additionally, models based on raw ECG signals have the potential to extract more detailed information.Reciever Operating Characteristic CurvesFeature Importance

Abstract BRCA-associated homologous recombination deficiency (HRD) is present in ~ 50% of high-grade serous carcinomas (HGSC) and predicts sensitivity to platinum-based therapy. However, there is little understanding of why some patients with BRCA-deficient tumors experience unexpectedly poor outcomes. We profiled 154 tumors, enriched for patients with BRCA-deficient tumors that experienced short overall survival (≤ 3 years, n = 42), using whole-genome, transcriptome, and methylation analyses. All but one BRCA-deficient tumor exceeded an accepted HRD genomic scarring threshold. However, patients with BRCA1-deficient HGSC with a more elevated HRD score survived significantly longer. Patients with BRCA2-deficient HGSC and loss of NF1 survived twice as long as those without NF1 loss, whereas PIK3CA or RAD21 amplification defined BRCA2-deficient HGSC with exceptionally short survival. BRCA1-deficient tumors in short survivors had evidence of immunosuppressive c-kit signaling and EMT. In a large HGSC cohort (n = 1,389) including 282 individuals with pathogenic germline BRCA variants (gBRCApv), the location of the mutation within functional domains stratified clinical outcomes. Notably, residual disease after primary surgery had limited prognostic effect in gBRCApv-carriers compared to non-carriers. Our findings indicate that tumor HR proficiency in the context of therapy response and survival is not a binary property, and highlight genomic and immune modifiers of outcomes in BRCA-deficient HGSC.

AbstractBackgroundCyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo‐ovarian high‐grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large‐scale, histotype‐specific validation has been performed. The hypothesis was that high‐level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC.MethodsWithin the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated.ResultsHigh‐level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high‐level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08‐1.47, p = .034, and HR, 1.18; 95% CI, 1.05‐1.32, p = .015, respectively). This was also true for cases with combined high‐level amplification/overexpression (HR, 1.26; 95% CI, 1.09‐1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1‐SD increase; 95% CI, 0.94‐1.06; p = .58). CCNE1 high‐level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss.ConclusionThis study provides large‐scale validation that CCNE1 high‐level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC.

Assessment of tumor-infiltrating lymphocytes (TILs) in histopathologic specimens can provide important prognostic information in diverse solid tumor types, and may also be of value in predicting response to treatments. However, implementation as a routine clinical biomarker has not yet been achieved. As successful use of immune checkpoint inhibitors and other forms of immunotherapy become a clinical reality, the need for widely applicable, accessible, and reliable immunooncology biomarkers is clear. In part 1 of this review we briefly discuss the host immune response to tumors and different approaches to TIL assessment. We propose a standardized methodology to assess TILs in solid tumors on hematoxylin and eosin sections, in both primary and metastatic settings, based on the International Immuno-Oncology Biomarker Working Group guidelines for TIL assessment in invasive breast carcinoma. A review of the literature regarding the value of TIL assessment in different solid tumor types follows in part 2. The method we propose is reproducible, affordable, easily applied, and has demonstrated prognostic and predictive significance in invasive breast carcinoma. This standardized methodology may be used as a reference against which other methods are compared, and should be evaluated for clinical validity and utility. Standardization of TIL assessment will help to improve consistency and reproducibility in this field, enrich both the quality and quantity of comparable evidence, and help to thoroughly evaluate the utility of TILs assessment in this era of immunotherapy.

Assessment of the immune response to tumors is growing in importance as the prognostic implications of this response are increasingly recognized, and as immunotherapies are evaluated and implemented in different tumor types. However, many different approaches can be used to assess and describe the immune response, which limits efforts at implementation as a routine clinical biomarker. In part 1 of this review, we have proposed a standardized methodology to assess tumor-infiltrating lymphocytes (TILs) in solid tumors, based on the International Immuno-Oncology Biomarkers Working Group guidelines for invasive breast carcinoma. In part 2 of this review, we discuss the available evidence for the prognostic and predictive value of TILs in common solid tumors, including carcinomas of the lung, gastrointestinal tract, genitourinary system, gynecologic system, and head and neck, as well as primary brain tumors, mesothelioma and melanoma. The particularities and different emphases in TIL assessment in different tumor types are discussed. The standardized methodology we propose can be adapted to different tumor types and may be used as a standard against which other approaches can be compared. Standardization of TIL assessment will help clinicians, researchers and pathologists to conclusively evaluate the utility of this simple biomarker in the current era of immunotherapy.

We previously identified a SNF1/AMPK-related protein kinase, Hunk, from a mammary tumor arising in an MMTV-neu transgenic mouse. The function of this kinase is unknown. Using targeted deletion in mice, we now demonstrate that Hunk is required for the metastasis of c-myc-induced mammary tumors, but is dispensable for normal development. Reconstitution experiments revealed that Hunk is sufficient to restore the metastatic potential of Hunk-deficient tumor cells, as well as defects in migration and invasion, and does so in a manner that requires its kinase activity. Consistent with a role for this kinase in the progression of human cancers, the human homologue of Hunk is overexpressed in aggressive subsets of carcinomas of the ovary, colon, and breast. In addition, a murine gene expression signature that distinguishes Hunk-wild type from Hunk-deficient mammary tumors predicts clinical outcome in women with breast cancer in a manner consistent with the pro-metastatic function of Hunk in mice. These findings identify a direct role for Hunk kinase activity in metastasis and establish an in vivo function for this kinase.