Therapeutic Decisions Research Articles

Performance du score de probabilité à 4 niveaux 4PEPS pour le diagnostic de l'embolie pulmonaire dans une population d'Afrique subsaharienne : données du Registre des Embolies Pulmonaires du Centre Hospitalier Universitaire de Bogodogo, Burkina Faso

ObjectiveTo evaluate the performance of the 4PEPS score in the diagnosis of pulmonary embolism at the University Hospital of Bogodogo from January 1, 2021 to July 31, 2023. MethodologyThis was a cross-sectional descriptive and analytical diagnostic study, running from January 1, 2021 to July 31, 2023. It took place in the infectious and tropical diseases departments, including a pneumology unit and a cardiology unit, of the Bogodogo University Hospital. Patients of both sexes with suspected pulmonary embolism who had undergone CT scan were included. The 4PEPS score was calculated and dichotomized into probable and improbable. It thus constituted the diagnostic test. CT scan was the gold standard. The accuracy of the diagnostic test was judged by the area under the ROC curve. An area under the curve between 0.70 and 1 would mean that the score was moderately informative to perfect. ResultsOur study included 472 patients with suspected pulmonary embolism out of a total population of 1228 patients. Hospital prevalence was 21.7%. The mean age of patients was 54.3 years. Females accounted for 52.1% of cases, with a sex ratio of 0.93. The prevalence of pulmonary embolism in the different probability levels of the 4PEPS score was 13.3% for the very low level, 11.7% for the low level, 84.6% for the intermediate level and 93.3% for the high level. Sensitivity and specificity were 92.1% and 86.82% respectively. The positive and negative predictive values were 90.1% and 89.4% respectively. The area under the ROC curve was 0.91. ConclusionIn our study, the 4PEPS score showed good negative and positive predictive values. The use of this score will enable practitioners faced with diagnostic difficulties to make therapeutic decisions, reducing inappropriate prescriptions for thoracic angioscan.

From Voxel to Gene: A Scoping Review on MRI Radiogenomics' Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas-The Promise of Virtual Biopsy?

Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies.

Prognostic Value of IMP3 Expression in Squamous Cervical Cancer.

Cervical cancer remains one of the leading causes of death from malignant diseases in women worldwide. Primary and secondary prevention have led to better outcomes in developed countries, whereas in developing countries, cervical cancer continues to be responsible for an unjustifiably high number of fatalities. The discovery of new tumor biomarkers can lead to earlier diagnosis, better therapeutic decisions, and improved treatment methods. IMP3 is a protein responsible for invasiveness and other aggressive characteristics of tumor processes. Its highly specific expression has been proven in various malignant processes. The level of IMP3 expression in cervical cancer cells could be used as a prognostic factor for a worse disease course. In this study, IMP3 expression was examined in 80 patients who underwent surgery for squamous cell cervical cancer in the first FIGO stage of the disease, and its association with disease-free period and overall survival was investigated. Data analysis did not show a statistically significant association between IMP3 expression and the mentioned primary outcomes, despite its association with clinical-pathological indicators of advanced disease. In conclusion, the analysis of IMP3 protein expression in patients with early-stage cervical cancer is of limited utility.

Comparison of methods for cancer stem cell detection in prognosis of early stages NSCLC

BackgroundDespite advances in diagnosis and treatment in lung cancer, therapies still fail to improve patient management due to resistance mechanisms and relapses. As Cancer stem cells (CSCs) directly contribute to tumor growth and therapeutic resistance, their clinical detection represents a major challenge. However specific and additional CSC markers lack. Thus, our aim was to achieve selective detection of CSCs with specific glycan patterns and assess the CSCs burden to predict the risk of relapse in NSCLC tumors.MethodsThe lung CSCs detection and sorting with a lectin MIX were assessed and compared to CD133 in vitro. Then, its putative role as CSC biomarker was evaluated in vivo and its clinical significance on 221 NSCLC patients.ResultsWe showed a significant CSCs enrichment in the MIX+ sorted fraction compared to CD133+ cells and confirmed its high tumorigenic capacity. The MIX prognostic value on the overall survival from early stages patients was validated suggesting its potential for detecting CSCs directly linked to tumor aggressiveness.ConclusionThe MIX could be more relevant for detecting and sorting CSCs than CD133. Moreover, its prognosis value could enable clinicians to better classify early-stage patients at high risk of relapse in order to tailor therapeutic decisions.

Hypertrophic obstructive cardiomyopathy-left ventricular outflow tract shapes and their hemodynamic influences applying CMR

Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiac disorders and is characterized by different phenotypes of left ventricular hypertrophy with and without obstruction. The effects of left ventricular outflow tract (LVOT) obstruction based on different anatomies may be hemodynamically relevant and influence therapeutic decision making. Cardiovascular magnetic resonance (CMR) provides anatomical information. We aimed to identify different shapes of LVOT-obstruction using Cardiovascular Magnetic Resonance (CMR). The study consisted of two parts: An in-vivo experiment for shape analysis and in-vitro part for the assessment of its hemodynamic consequences. In-vivo a 3D depiction of the LVOT was created using a 3D multi-slice reconstruction from 2D-slices (full coverage cine stack with 7 slices and a thickness of 5–6 mm with no gap) in 125 consecutive HOCM patients (age = 64.17 +/− 12.655; female n = 42). In-vitro an analysis of the LVOT regarding shape and flow behavior was conducted. For this purpose, 2D and 4D measurements were performed on 3D printed phantoms which were based on the anatomical characteristics of the in-vivo study, retrospectively. The in-vivo study identified three main shapes named K- (28.8%), X- (51.2%) and V-shape (10.4%) and a mixed one (9.6%). By analyzing the in-vitro flow measurements every shape showed an individual flow profile in relation to the maximum velocity in cm/s. Here, the V-shape showed the highest value of velocity (max. 138.87 cm/s). The X-shape was characterized by a similar profile but with lower velocity values (max. 125.39 cm/s), whereas the K-shape had an increase of the velocity without decrease (max. 137.11 cm/s). For the first time three different shapes of LVOT-obstruction could be identified. These variants seem to affect the hemodynamics in HOCM.

Histologic healing and clinical outcomes in ulcerative colitis.

Growing evidence suggests histologic healing (HH) improves clinical outcomes in ulcerative colitis (UC) patients beyond endoscopic healing (EH). We hypothesize that HH is associated with better clinical outcomes in Asian UC patients, for whom data is lacking. We performed a retrospective study of UC patients in clinical remission (CR) with a follow-up colonoscopy and minimum 1-year follow-up post-colonoscopy. Primary outcome was clinical relapse (CRL), defined as either a Simple Clinical Colitis Activity Index score of > 2, medication escalation, hospitalization or colectomy. Predictors of CRL and HH were assessed. One hundred patients were included with a median follow-up of 22 months. At index colonoscopy, 80 patients were in EH. On follow-up, 41 patients experienced CRL. Of 80 patients in EH, 34 (42.5%) had persistent histologic activity (Nancy Index ≥ 2) and 29 (36.3%) relapsed during the follow-up period. Amongst patients in CR and EH, those with HH had lower CRL rate (26.1% vs. 50.0%, P= 0.028) and longer CRL-free survival (mean 46.1 months vs. 31.5 months, P= 0.015) than those with persistent histologic activity. On bivariable analysis of 100 patients in CR, HH, and Mayo endoscopic score (MES) of 0 were significantly associated with lower risk of CRL. On multivariable analysis, only MES 0 remained predictive of lower CRL risk. Above and beyond CR and EH, achieving HH improves clinical outcomes in Asian UC patients. However, HH may not confer incremental benefit if MES 0 has been achieved. Further prospective studies evaluating the benefit of histologically guided therapeutic decisions are needed.

Ferritin, inflammation, and iron deficiency in acute heart failure: evidence from the EDIFICA cohort

Abstract Background Ferritin is commonly used to evaluate iron stores and guide therapeutic decisions regarding intravenous iron supplementation. However, in the context of AHF, inflammation-driven upregulation of ferritin might disrupt its correlation with iron stores, restricting iron bioavailability and potentially amplifying the inflammatory response. Aim This study aims to assess the clinical and prognostic associations of ferritin levels in an AHF cohort and to determine whether the prognostic value of ferritin is influenced by the presence of infection, inflammatory activation, and other markers of iron deficiency. Methods The association between ferritin and clinical outcomes (180 days) in AHF was evaluated in a cohort of 526 patients from the EDIFICA registry. Results The median ferritin plasma concentration at admission was 180 pg/mL. Patients with higher ferritin levels at admission were predominantly men, exhibiting a high prevalence of chronic kidney disease and alcohol consumption, and presenting with lower blood pressure and a higher incidence of clinical infection. Higher ferritin levels were associated with increased risk of the composite of heart failure hospitalization or cardiovascular death (Tertile 2: HR 1.75; 95% CI 1.10–2.79; p = 0.017; Tertile 3: HR 1.79; 95% CI 1.08–2.97; p = 0.025), independently of classical HF prognostic factors, inflammatory and iron-related markers. No significant associations were found between admission serum iron or transferrin saturation tertiles, iron status categories, or guideline-defined iron deficiency (ID) criteria and the primary composite outcome. However, at discharge, patients who met the criteria for defective iron utilization, low iron storage, or guideline-defined ID had a lower risk of the composite endpoint compared to those with normal iron utilization or who did not meet the guideline-defined ID criteria, respectively. Conclusions Elevated ferritin levels are independently associated with poor prognosis in AHF. Low ferritin levels are associated with a favorable outcome and do not carry significant value in identifying ID in this population.

Assessment of decision-making autonomy in chronic pain patients: a pilot study

BackgroundPatient decision-making autonomy refers to the patients’ ability to freely exert their own choices and make their own decisions, given sufficient resources and information to do so. In pain medicine, it is accepted that appropriate beneficial management aims to propose an individualized treatment plan shared with the patients, as agents, to help them live as autonomously as possible with their pain. However, are patients in chronic pain centers sufficiently autonomous to participate in the therapeutic decisions that concern them? As this question still remains unanswered, a pilot study was set up to that aim.MethodsOver a 2-month period, first-time patients within a tertiary multidisciplinary pain center underwent a systematic evaluation of their autonomy using the MacArthur Competence Assessment Tool for Treatment (MacCAT-T), considered the benchmark tool for measuring a patient’s ability to consent to treatment. Demographic data and pain characteristics of the patients were collected and their respective attending pain physicians were asked to clinically assess their patients’ degree of autonomy. Another physician, who had not participated in the initial patient evaluation, subsequently administered the MacCAT-T questionnaire to the same patients.ResultsTwenty-seven patients were included during the study period (21 women and 6 men), with an average age of 50 years. The average duration of pain was 8 years. Based on their clinical experience, the 4 different pain physicians in charge of these patients considered that out of 25 assessed patients, 22 of them (89%) had full decision-making capacity, with no deficit in autonomy. According to the MacCAT-T results, only 13 of these 25 patients (48%) had no deficit, while 7 (26%) had a major deficit in autonomy. The only patient characteristic that appeared to be related to autonomy was pain type, specifically nociplastic pain. The average time taken to complete the test was 20 min, and patients were very satisfied with the interview.ConclusionResults from the present pilot study suggest that patients suffering from chronic pain do not appear to be entirely autonomous in their decision to consent to the proposed treatment plan according to the MacCAT-T questionnaire, and physicians seem to find it difficult to properly assess this competence in a clinical setting. Further studies with larger samples are needed to better evaluate this concept to improve the complex management of these patients.

Contribution of [18F]FET PET in the Management of Gliomas, from Diagnosis to Follow-Up: A Review.

Gliomas, the most common type of primary malignant brain tumors in adults, pose significant challenges in diagnosis and management due to their heterogeneity and potential aggressiveness. This review evaluates the utility of O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET) positron emission tomography (PET), a promising imaging modality, to enhance the clinical management of gliomas. We reviewed 82 studies involving 4657 patients, focusing on the application of [18F]FET in several key areas: diagnosis, grading, identification of IDH status and presence of oligodendroglial component, guided resection or biopsy, detection of residual tumor, guided radiotherapy, detection of malignant transformation in low-grade glioma, differentiation of recurrence versus treatment-related changes and prognostic factors, and treatment response evaluation. Our findings confirm that [18F]FET helps delineate tumor tissue, improves diagnostic accuracy, and aids in therapeutic decision-making by providing crucial insights into tumor metabolism. This review underscores the need for standardized parameters and further multicentric studies to solidify the role of [18F]FET PET in routine clinical practice. By offering a comprehensive overview of current research and practical implications, this paper highlights the added value of [18F]FET PET in improving management of glioma patients from diagnosis to follow-up.

Discrepancies between treatment preference and effectiveness

AbstractInvolving clients in therapeutic decisions is a crucial component of effective and ethical behavior‐analytic practice. However, discrepancies between client preference for treatment options and treatment effectiveness are possible in the course of treatment. The purpose of this paper is to advance the discussion of discrepancies between client preference for treatment options and treatment effectiveness by (a) reviewing relevant considerations for adapted strategies to involve clients with limited prerequisite skills in therapeutic decisions, (b) presenting clinical data that exemplifies discrepancies between client preference for treatment options and treatment effectiveness, (c) describing nuanced considerations for navigating discrepancies, and (d) providing suggestions for future research on this topic.

Detection rate of gastrin-releasing peptide receptor (GRPr) targeted tracers for positron emission tomography (PET) imaging in primary prostate cancer: a systematic review and meta-analysis.

The gastrin-releasing peptide receptor (GRPr) has gained recognition as a promising target for both diagnostic and therapeutic applications in a variety of human cancers. This study aims to explore the primary tumor detection capabilities of [68Ga] Ga-GRPr PET imaging, specifically in newly diagnosed intra-prostatic prostate cancer lesions (PCa). Following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies) guidelines, a systematic literature search was conducted using the Medline, Embase, Scopus, and Web of Science databases. Data regarding patient characteristics and imaging procedure details-including the type of radiotracer used, administered activity, image acquisition time, scanner modality, criteria, and detection rate of index test-were extracted from the included studies. The pooled patient-and lesion-based detection rates, along with their corresponding 95% confidence intervals (CI), were calculated using a random effects model. The final analysis included 9 studies involving 291 patients and 350 intra-prostatic lesions with [68Ga] Ga-GRPr PET imaging in primary PCa. In per-patient-based analysis of [68Ga] Ga-GRPr PET imaging, the pooled detection rates of overall and patients with Gleason score ≥ 7 were 87.09% (95% CI 74.98-93.82) and 89.01% (95% CI 68.17-96.84), respectively. In per-lesion-based analysis, the pooled detection rate [68Ga] Ga-GRPr PET imaging was 78.54% (95% CI 69.8-85.29). The pooled detection rate mpMRI (multiparametric magnetic resonance imaging) in patient-based analysis was 91.85% (95% CI 80.12-96.92). The difference between the detection rates of the mpMRI and [68Ga] Ga-GRPr PET imaging was not statistically significant (OR 0.90, 95% CI 0.23-3.51). Our findings suggest that [68Ga] Ga-GRPr PET imaging has the potential as a diagnostic target for primary PCa. Future research is needed to determine the effectiveness of [68Ga] Ga-GRPr PET in delivering additional imaging data and guiding therapeutic decisions.

Kommunikation bei Demenz

A scoping review investigated the effectiveness of different communicative interventions for patients with dementia. It demonstrated that communication between caregivers and dementia patients is improved by music, multisensory stimulation, and communication training. As a result agitation, mania, fear, anger and corrective touching are reduced, and fluency, spontaneous speech, eye contact, smiling and affective touching are enhanced. The recommendations, summarized in a fact sheet, also contribute to improved therapeutic decision making with bystanders and oral health of people with dementia.

Abstract B066: An AI approach to unraveling treatment response in pancreatic cancer: Insights from the COMPASS trial leveraging large language models (LLMs)

Abstract Pancreatic cancer, often termed the “silent killer” due to its vague symptoms and late diagnosis, continues to challenge oncologists with its significant treatment challenges and poor prognosis. Standard-of-care regimens, such as FOLFIRINOX (FFX) and Gemcitabine + nab-paclitaxel (GnP), show varied efficacy among patients. Understanding the factors that contribute to these varied responses is crucial for advancing personalized treatment strategies. This study uses NetraAI, a novel machine learning (ML) approach to analyze whole genome sequencing (WGS) and RNA sequencing (RNASeq) data from pancreatic ductal adenocarcinoma (PDAC) subjects (n=208) prior to FFX or GnP first-line combination therapy from the COMPASS trial, aiming to identify key variables that distinguish between responses to these two therapeutic regimens. NetraAI identifies explainable subpopulations within clinical trial data defined by a set of driving variables (i.e., hypotheses) related to drug response. This approach focuses on explaining only subjects that collectively represent a real effect and does not forcibly explain everyone, to avoid overfitting. Our analysis identified several key factors significantly correlating with treatment response. Downregulated LRRC8E and upregulated PTPRH showed favourable response to GnP, while upregulated UTG1A13p was associated with FFX response. Across all patients (stable disease, partial response, complete response) CLEC19A and LRRC29 expression were associated with better response to both FFX and GnP, suggesting that they may play a role in the chemotherapy response mechanisms. The NetraGPT module, leverages large language models (LLMs) and NetraAI insights to further query trial results. Querying NetraGPT, we identified a link between LRRC29 and HOOK1, associated with SHP, which has been linked to GnP response in non-small cell lung cancer. These findings suggest that chemotherapy response may be cancer-agnostic, depending on the chemotherapeutic agent, with LRRC29 expression discriminating selecting GnP versus FFX treatment. Our findings highlight the potential of AI in elucidating complex relationships within clinical trial data, offering valuable insights into the determinants of treatment response in pancreatic cancer to achieve more personalized cancer care. The identified biomarkers provide a promising foundation for developing personalized treatment strategies, enhancing the precision of therapeutic decisions. Integrating explainable ML methods for small and medium sized clinical data with LLMs provides an exciting step forward. Retrieval Augmentation allows researchers to create LLMs that are designed to answer questions about heterogeneous patient populations through the vantage of the various insights that have been discovered via specialized ML methods such as NetraAI and its NetraGPT module. We will provide a demo of how an interactive LLM was used to derive new insights into PDAC that may pave the way to precision treatment and explain how researchers can begin to create their own versions of these tools. Citation Format: Joseph Geraci, Bessi Qorri, Mike Tsay, Christian Cumbaa, Paul Leonchyk, Larry Alphs, Luca Pani. An AI approach to unraveling treatment response in pancreatic cancer: Insights from the COMPASS trial leveraging large language models (LLMs) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B066.

Analysis of Trends in the Use of Artificial Intelligence in Diagnosis and Treatment

AI in healthcare has improved, making diagnostics more accurate and increasing the effectiveness of treatments. The present study discusses the AI trends in diagnostic and therapeutic applications and focuses on the presented practical applications and their effects on patient care. The purpose of this particular review is to focus on the current developments in the implementation of AI in the field of health care, present main use cases and successes, as well as discuss about the issues and concerns in the topic at hand. Previous studies on AI in healthcare with specific consideration of diagnostic image analysis and interpretation, histology and molecular pathology, whole-genome sequencing, and therapeutic decision support are discussed. The selection criteria included papers with data gathered from real-life AI cases and quantitative findings. Study materials were obtained from e-journals, conference papers, and established online sources with descriptive analysis being done on the data collected. A summary of the findings revealed a number of highly impactful subcategories focused on the use of artificial intelligence diagnostic imaging, especially in radiology, pathology, and genomics. The AI applications used in the fields of operations and drug discovery revealed the ability to accurately predict clinical trial outcomes and to create effective treatments. First of all, AI can become a game changer in healthcare by enhancing diagnostics accuracy and treatment outcomes. The future research questions include further developing the methods that explain the AI models’ decisions, protecting the privacy of patient information, and reducing algorithmic bias for better fair healthcare for all. Therefore, better interactions between creators of AI and clinicians and regulatory authorities are pertinent to make sure that the full advantages of AI are realized in clinical practice to advance patient care.

Determinants associated with the prescription of a first biologic therapy in patients with axial spondyloarthritis and concomitant fibromyalgia in daily practice

ObjectivesThere is no consensus on the therapeutic strategy of rheumatologists for patients with spondyloarthritis (SpA) and concomitant fibromyalgia (FM). The main aim of this study was to identify, in a population of rheumatologists practicing in Normandy, France, the determinants associated with their decision to prescribe a first biologic DMARD (bDMARD) in patients with Spa/FM. Specific objectives were to evaluate professional prescribing practices to identify a set of criteria likely to contribute to the therapeutic decision of rheumatologists, and to validate the relevance of these criteria. MethodThis is a cross-sectional survey-based study using a mixed (qualitative and quantitative) method. The quantitative approach was web-based and conducted among rheumatologists in Normandy. ResultsThe qualitative study allowed us to identify a set of criteria likely to contribute to the therapeutic decision of rheumatologists. In the quantitative study, 54/113 rheumatologists filled the questionnaire. Four criteria were considered by all respondents to contribute to their decision to prescribe a first bDMARD: arthritis on physical examination, extra-articular manifestations, systemic inflammation and structural damage on imaging. ConclusionsThe determinants associated with the decision of rheumatologists to prescribe a first bDMARD in patients with SpA/FM were mostly objective, in line with the recommendations in the literature. Most criteria were more related to an approach aimed at ensuring the diagnosis of SpA than evaluating its activity or severity.

Development and application of a rapid visual detection technique for VanA gene in vancomycin-resistant Enterococcus faecium.

One of the key approaches to treating and controlling vancomycin-resistant Enterococcus faecium (VREFm) is an accurate and rapid diagnosis. To achieve this goal, a simple and rapid method must be constructed for immediate detection in the field. Multienzyme isothermal rapid amplification (MIRA) is an isothermal rapid amplification method that allows amplification reactions to be completed under room temperature conditions. When combined with lateral flow strips (LFSs), MIRA-LFS enables the rapid detection of pathogenic microorganisms. However, the MIRA method often produces false signals. These false signals are eliminated by using base mismatches introduced in primers and probes. The MIRA-LFS system was constructed with high specificity and sensitivity for the detection of VREfm, without the limitation of sophisticated instruments. This enables the prompt formulation of diagnostic and therapeutic decisions.

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma.

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell's concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan-Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan-Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors.

Construct prognostic models of multiple myeloma with pathway information incorporated.

Multiple myeloma (MM) is a hematological disease exhibiting aberrant clonal expansion of cancerous plasma cells in the bone marrow. The effects of treatments for MM vary between patients, highlighting the importance of developing prognostic models for informed therapeutic decision-making. Most previous models were constructed at the gene level, ignoring the fact that the dysfunction of the pathway is closely associated with disease development and progression. The present study considered two strategies that construct predictive models by taking pathway information into consideration: pathway score method and group lasso using pathway information. The former simply converted gene expression to sample-wise pathway scores for model fitting. We considered three methods for pathway score calculation (ssGSEA, GSVA, and z-scores) and 14 data sources providing pathway information. We implemented these methods in microarray data for MM (GSE136324) and obtained a candidate model with the best prediction performance in interval validation. The candidate model is further compared with the gene-based model and previously published models in two external data. We also investigated the effects of missing values on prediction. The results showed that group lasso incorporating Vax pathway information (Vax(grp)) was more competitive in prediction than the gene model in both internal and external validation. Immune information, including VAX pathways, seemed to be more predictive for MM. Vax(grp) also outperformed the previously published models. Moreover, the new model was more resistant to missing values, and the presence of missing values (<5%) would not evidently deteriorate its prediction accuracy using our missing data imputation method. In a nutshell, pathway-based models (using group lasso) were competitive alternatives to gene-based models for MM. These models were documented in an R package (https://github.com/ShuoStat/MMMs), where a missing data imputation method was also integrated to facilitate future validation.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study.

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

Immune-related cell death index and its application for hepatocellular carcinoma

Regulated cell death (RCD) plays a crucial role in the immune microenvironment, development, and progression of hepatocellular carcinoma (HCC). However, reliable immune-related cell death signatures have not been explored. In this study, we collected 12 RCD modes (e.g., apoptosis, ferroptosis, and cuproptosis), including 1078 regulators, to identify immune-related cell death genes based on HCC immune subgroups. Using a developed competitive machine learning framework, nine genes were screened to construct the immune-related cell death index (IRCDI), which is available for online application. Multi-omics data, along with clinical features, were analyzed to explore the HCC malignant heterogeneity. To validate the efficacy of this model, more than 18 independent cohorts, including survival and diverse treatment cohorts and datasets, were utilized. These findings were further validated using in-house samples and molecular biological experiments. Overall, the IRCDI may have a wide application in individual therapeutic decision-making and improving outcomes for HCC patients.