Individual Treatment Decisions Research Articles

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.

Accurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade. A total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice. The intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort. This study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

A Predictive Noninvasive Single-Nucleotide Variation-Based Biomarker Signature for Resectable Pancreatic Cancer: Protocol for a Prospective Validation Study.

Single-nucleotide variations (SNVs; formerly SNPs) are inherited genetic variants that can be easily determined in routine clinical practice using a simple blood or saliva test. SNVs have potential to serve as noninvasive biomarkers for predicting cancer-specific patient outcomes after resection of pancreatic ductal adenocarcinoma (PDAC). Two recent analyses led to the identification and validation of three SNVs in the CD44 and CHI3L2 genes (rs187115, rs353630, and rs684559), which can be used as predictive biomarkers to help select patients most likely to benefit from pancreatic resection. These variants were associated with an over 2-fold increased risk for tumor-related death in three independent PDAC study cohorts from Europe and the United States, including The Cancer Genome Atlas cohorts (reaching a P value of 1×10-8). However, these analyses were limited by the inherent biases of a retrospective study design, such as selection and publication biases, thereby limiting the clinical use of these promising biomarkers in guiding PDAC therapy. To overcome the limitations of previous retrospectively designed studies and translate the findings into clinical practice, we aim to validate the association of the identified SNVs with survival in a controlled setting using a prospective cohort of patients with PDAC following pancreatic resection. All patients with PDAC who will undergo pancreatic resection at three participating hospitals in Switzerland and fulfill the inclusion criteria will be included in the study consecutively. The SNV genotypes will be determined using standard genotyping techniques from patient blood samples. For each genotyped locus, log-rank and Cox multivariate regression tests will be performed, accounting for the relevant covariates American Joint Committee on Cancer stage and resection status. Clinical follow-up data will be collected for at least 3 years. Sample size calculation resulted in a required sample of 150 patients to sufficiently power the analysis. The follow-up data collection started in August 2019 and the estimated end of data collection will be in May 2027. The study is still recruiting participants and 142 patients have been recruited as of November 2023. The DNA extraction and genotyping of the SNVs will be performed after inclusion of the last patient. Since no SNV genotypes have been determined, no data analysis has been performed to date. The results are expected to be published in 2027. This is the first prospective study of the CD44 and CHI3L2 SNV-based biomarker signature in PDAC. A prospective validation of this signature would enable its clinical use as a noninvasive predictive biomarker of survival after pancreatic resection that is readily available at the time of diagnosis and can assist in guiding PDAC therapy. The results of this study may help to individualize treatment decisions and potentially improve patient outcomes. DERR1-10.2196/54042.

Abstract PO5-15-11: A Novel Biosignature to Predict Radiation Therapy Response in Early-Stage Invasive Breast Cancer Treated with Breast-Conserving Surgery

Abstract Introduction: Radiation therapy (RT) is a standard component of treatment for most patients with early-stage, hormone receptor-positive, invasive breast cancer (BC) following breast-conserving surgery (BCS). RT recommendations are primarily based on clinicopathologic (CP) factors, such as tumor size, nodal status, and histological grade. While these factors provide prognostic information, their ability to accurately identify patients at a higher risk of ipsilateral breast recurrence (IBR) or those who will significantly benefit from RT is unclear. Therefore, incorporation of additional predictive markers beyond CP factors is warranted and there remains a need to refine the selection criteria for patients who will derive the greatest benefit from RT. In this study, we aimed to explore the role of immune and metabolic signaling axes as potential predictive markers for RT response in hormone receptor-positive breast cancer patients. Methods: A cohort of 939 women from Sweden were diagnosed with BC between 1987 and 2004. A subset of 471 patients was diagnosed with T1-T2N0M0 BC and underwent BCS with negative margins. Patients were treated with BCS plus/minus RT (median dose 50Gy) based upon physician preference. Biomarkers were assayed in formalin-fixed paraffin-embedded tissue micro arrays using multiplex immunofluorescence and multi-spectral imaging in a CLIA lab (Laguna Hills, CA). Immune and metabolic axes were defined employing biomarkers and used to classify patients into three risk groups: 1) Low Risk (lower baseline risk and no RT benefit), 2) Elevated Risk (higher baseline risk and significant RT benefit), and 3) Residual Risk (higher baseline risk and suboptimal RT benefit). Risk groups were analyzed for IBR rate using Kaplan-Meier and Cox proportional hazards analyses. Risk group prognosis and RT prediction was assessed for RT-risk group interaction in multivariable analysis, adjusting for CP factors. Results: Among the patients with complete biomarker data, 261 were hormone receptor-positive, and had a median follow-up of 140 months. Median age was 62 years (28-88 years), and median tumor size was 1.1 cm (0.1-4 cm). Some patients (21%) received endocrine therapy (ET), and most (76%) received RT, with 79% of patients who received ET also receiving RT. In the overall population, RT following BCS was associated with a reduction in HR for recurrence (HR=0.3, 95%CI [0.1, 0.5], p< 0.001). Patients classified as Low Risk (41%) did not have a significant benefit from RT (HR=0.9, 95%CI [0.1, 8.3], p=0.95) in multivariable analysis adjusted for CP factors. For patients who were classified as Elevated Risk (31%), multivariable analysis showed a higher IBR rate without RT (HR=5.7, 95%CI [1.3, 25.1], p< 0.021) relative to the Low Risk group who had a significant benefit from RT (HR=0.1, 95%CI [0.02, 0.2], p< 0.001). Patients classified into the Residual Risk group (28%) had no significant reduction from RT (HR=0.7, 95%CI [0.2, 2.0], p=0.61). However, in multivariable analysis adjusted for CP factors, patients treated with RT in the Residual Risk group had higher IBR rates (HR=3.7, 95%CI [1.6, 8.7], p=0.0026) compared to RT-treated patients in other risk groups. The biosignature was predictive for RT benefit in multivariable analysis adjusted for CP factors (p-interaction < 0.001). Conclusions:The biosignature used in this study demonstrated the potential to identify patients who may derive limited benefit from RT, allowing for tailored therapeutic approaches. These findings underscore the importance of individualized treatment decisions based on risk stratification. Further clinical validation and refinement of risk stratification strategies are warranted to optimize outcomes in hormone receptor-positive, BC patients undergoing BCS. Citation Format: Troy Bremer, Karuna Mittal, Chirag Shah, Frank Vicini, Steven Shivers, Charlotta Wadsten. A Novel Biosignature to Predict Radiation Therapy Response in Early-Stage Invasive Breast Cancer Treated with Breast-Conserving Surgery [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-11.

Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease.

Personalized approaches to care are increasingly common in clinical nephrology. Although risk prediction models are developed to estimate the risk of kidney-disease related outcomes, they infrequently consider the priorities of patients they are designed to help. This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature. Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.

Long-term Outcome of Asymptomatic Esophageal Eosinophilia.

: Asymptomatic esophageal eosinophilia (aEE), characterized by eosinophil infiltration in the esophagus without clinical symptoms, has been reported as a precursor of eosinophilic esophagitis (EoE). Nevertheless, no report exists on the long-term clinical course of the disease. Therefore, this study aimed to investigate the long-term clinical course of aEE over 5 years, including the symptomatic conversion rate and the effect of treatments. : We reviewed 28 patients with aEE who had been followed up for over 5 years with endoscopic monitoring. The basal characteristics of patients were compared with those of 58 patients diagnosed with EoE during the same period. Patients' clinicopathological findings were collected and examined. : No significant differences in basal characteristics and histopathological findings were observed between the patients with aEE and those with EoE. The median follow-up duration was 64 months. Among the 28 patients with aEE, seven were treated with proton pump inhibitor or potassium-competitive acid blocker and the remaining 21 opted for follow-up with no medication. Among the treated patients, six (85.7%) exhibited endoscopic and pathologic improvements. Among the cases followed up without medication, the findings worsened in two (9.5%), improved spontaneously in seven (33.3%), and were unchanged in 12 (57.1%), and three (14.3%) developed symptoms at a mean time of 40 months. Symptoms developed in cases where endoscopic and pathologic findings remained unchanged or worsened during follow-up. : Some patients with aEE had improved findings without treatment, whereas others developed symptoms, emphasizing the importance of long-term monitoring and individualized treatment decisions.

Prognostic factors for disease activity in newly diagnosed teriflunomide-treated patients with multiple sclerosis: a nationwide Danish study

BackgroundClinicians frequently rely on relapse counts, T2 MRI lesion load (T2L) and Expanded Disability Status Scale (EDSS) scores to guide treatment decisions for individuals diagnosed with multiple sclerosis (MS). This...

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study

Rationale and ObjectivesThe aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). Materials and MethodsIn this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. ResultsThe DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. ConclusionThe DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: A multicenter study.

Microsatellite instability (MSI) is associated with treatment response and prognosis in patients with rectal cancer (RC). However, intratumoral heterogeneity limits MSI testing in patients with RC. We developed a subregion radiomics model based on multiparametric magnetic resonance imaging (MRI) to preoperatively assess high-risk subregions with MSI and predict the MSI status of patients with RC. This retrospective study included 475 patients (training cohort, 382; external test cohort, 93) with RC from two participating hospitals between April 2017 and June 2023. In the training cohort, subregion radiomic features were extracted from multiparametric MRI, which included T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. MSI-related subregion radiomic features, classical radiomic features, and clinicoradiological variables were gathered to build five predictive models using logistic regression. Kaplan-Meier survival analysis was conducted to explore the prognostic information. Among the 475 patients (median age, 64 years [interquartile range, IQR: 55-70 years];304 men and 171 women), the prevalence of MSI was 11.16% (53/475). The subregion radiomics model outperformed the classical radiomics and clinicoradiological models in both training (area under the curve [AUC]=0.86, 0.72, and 0.59, respectively) and external test cohorts (AUC=0.83, 0.73, and 0.62, respectively). The subregion-clinicoradiological model combining clinicoradiological variables and subregion radiomic features performed the optimal, with AUCs of 0.87 and 0.85 in the training and external test cohorts, respectively. The 3-year disease-free survival rate of MSI groups predicted based on the model was higher than that of the predicted microsatellite stability (MSS) groups in both patient cohorts (training, P=0.032; external test, P=0.046). We developed and validated a model based on subregion radiomic features of multiparametric MRI to evaluate high-risk subregions with MSI and predict the MSI status of RC preoperatively, which may assist in individualized treatment decisions and positioning for biopsy.

Timing of first abdominal operation in Crohn’s disease based on a diagnostic model

This study aims to develop a clinical diagnostic model for assessing the need for initial abdominal surgery in patients diagnosed with Crohn's disease (CD) and create a nomogram to facilitate clinical decision-making. A total of 164 surgical CD patients and 230 control CD patients were included in this retrospective analysis. Least Absolute Shrinkage and Selection Operator (Lasso) regression and binomial logistic regression were employed to select clinical variables. The 394 CD patients were randomly allocated to a training set and a validation set in a 7:3 ratio. The filtered variables were used to establish a diagnostic model and nomogram in the training set, subsequently validated in the testing set. Decision Curve Analysis (DCA) and clinical impact curve were constructed to validate the clinical applicability of the model. Binomial logistic regression analysis identified seven clinical variables with a p-value less than 0.01, including Biomarker (B), Waist-to-Height Ratio (WHtR), Intestinal obstruction, Albumin (ALB), Crohn's Disease Activity Index (CDAI), Myocardial Flow Index (MFI), and C-reactive protein (CRP). These variables were utilized to establish the diagnostic model. Calibration curves showed good alignment, with a C-index of 0.996 in the training set and 0.990 in the testing set. DCA and clinical impact curve demonstrated that the diagnostic model had good clinical efficiency and could provide clinical benefits. A validated diagnostic model for determining the timing of the first abdominal operation in CD patients was established and evaluated, showing high discriminative ability, calibration, and clinical efficiency. It can be utilized by clinicians to assess the optimal timing for transitioning CD patients from medical treatment to surgical intervention, providing valuable references for individualized treatment decisions for CD patients.

Treatment of psoriasis vulgaris : Therapy strategies for optimal patient-centered care

Psoriasis is achronic inflammatory systemic disease that requires optimal long-term management due to its high prevalence in the population and the numerous comorbidities that severely impair quality of life. Avariety of treatment options are now available. In addition to objective skin findings and aspecific location such as nails or genital area, the presence of psoriatic arthritis and other comorbidities as well as the disease burden of the affected person play adecisive role in individualized treatment decision-making. Good communication with the patient is fundamental to understand the individual needs and expectations of the patient. Shared decision-making can positively influence adherence and thus also the clinical outcome and patient satisfaction. In addition, interdisciplinary collaboration is crucial and often necessary for acomprehensive therapy strategy.

Sex impacts treatment decisions in multiple sclerosis.

Individual disease-modifying treatment (DMT) decisions might differ between female and male people with MS (pwMS). To identify sex-related differences in DMT strategies over the past decades in a real-world setting. In this cohort study, data from the Austrian Multiple Sclerosis Treatment Registry (AMSTR), a nationwide prospectively collected registry mandatory for reimbursement, were retrospectively analyzed. Of 4840 pwMS, those with relapsing-remitting MS, aged at least 18years, who started DMT and had at least two clinical visits, were identified. At baseline, demographics, Expanded Disability Status Scale (EDSS) score, annualized relapse rate (ARR) in the prior 12months and MRI lesion load were assessed. At follow-up, ARR, EDSS scores, and DMT were determined. A total of 4224 pwMS were included into the study and had a median of 10 (IQR 5-18) clinical visits over an observation period of 3.5 (IQR 1.5-6.1) years. Multivariable Cox regression analysis revealed that the probability of DMT escalation due to relapse activity was lower in female than male pwMS (HR 4.1 vs. 8.3 per ARR). Probability of discontinuing moderate-effective DMT was higher in female pwMS when they were younger (HR 1.03 per year), and lower in male pwMS at higher age (HR 0.92). Similarly, female pwMS were more likely to stop highly effective DMT than male pwMS (HR 1.7). Among others, the most frequent reason for DMT discontinuation was family planning in female pwMS. All sex-related effects were independent of disease activity, such as MRI lesion load, baseline ARR or EDSS. Real-world treatment decisions are influenced by sex-related aspects. Awareness of these associations should prevent unwarranted differences in MS care.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Effects of high tibial osteotomy compared with unicondylar knee arthroplasty on the surgical site wound infection and pain in patients with medial knee osteoarthritis.

This study aims to comprehensively compare the effects of unicondylar knee arthroplasty (UKA) and high tibial osteotomy (HTO) on wound infection and pain in patients with medial knee osteoarthritis. A computerized search was conducted in Embase, PubMed, Google Scholar, China National Knowledge Infrastructure, Cochrane Library and Wanfang databases, from database inception to October 2023, for studies comparing UKA and HTO for medial knee osteoarthritis. Studies selection, data extraction and study quality evaluation were independently conducted by two researchers. Stata 17.0 software was employed for data analysis. Overall, 10 studies involving 870 patients with medial knee osteoarthritis were included. It was found that the UKA group had significantly lower wound visual analogue scale scores compared to the HTO group (SMD = -0.53, 95%CI: -0.87 to -0.20, p < 0.001). The incidence of wound infection in the UKA group was higher than in the HTO group (OR = 1.92, 95%CI: 0.65-5.69, p = 0.240), and the incidence of complications was lower (OR = 0.89, 95%CI: 0.52-1.54, p = 0.684), though these differences were not statistically significant. This study indicates that UKA is effective in alleviating postoperative wound pain in medial knee osteoarthritis. However, the rates of postoperative wound infection and complications are comparable to those of HTO. Clinicians should consider factors such as patient age and disease severity in making individualized treatment decisions.

A Comparative Analysis of Locking Plate versus Intramedullary Nail for Humerus Shaft Fractures

Introduction: Humerus shaft fractures pose a significant challenge in Orthopaedic practice, with various treatment approaches available, including surgical methods like conservative management, Plating and Nailing. The choice between these methods remains debatable, necessitating further comparative studies. Methods: This study compared Open Reduction Internal Fixation (ORIF) with Locking Compression Plates and Closed Reduction Internal Fixation (CRIF) with Intramedullary Interlocking nails for Humerus shaft fractures. Patients were categorized into two groups based on the intervention received. Perioperative parameters, radiological outcomes, and complications were assessed. Results: Baseline demographics were comparable between the two groups. The Plating Group had longer surgery duration and higher blood loss compared to the Nailing Group. Both groups had similar rates of union and non-union, with no significant difference in shoulder function and pain outcomes at one year post-operation. Discussion: Functional outcomes post-union remain acceptable despite notable deformities, suggesting the viability of conservative treatments. While ORIF with plating has been conventional, concerns over blood loss and nerve injury have led to exploration of CRIF with intramedullary nails. This study, consistent with previous findings, found no significant disparity in outcomes between the two methods, emphasizing the need for personalized treatment decisions. In conclusion, this study adds to the understanding of Humerus shaft fracture management, highlighting the need for individualized treatment decisions and further research in this field.

Risdiplam improves subjective swallowing quality in non-ambulatory adult patients with 5q-spinal muscular atrophy despite advanced motor impairment

Background5q-associated spinal muscular atrophy (SMA) is characterized by the progressive loss of motor neurons with consecutive weakness and atrophy of the limb, respiratory, and bulbar muscles. While trunk and limb motor function improve or stabilize in adults with SMA under nusinersen and risdiplam treatment, the efficacy on bulbar function in this age group of patients remains uncertain. However, it is important to assess bulbar dysfunction, which frequently occurs in the disease course and is associated with increased morbidity and mortality.MethodsBulbar function was evaluated prospectively in 25 non-ambulatory adults with type 2 and 3 SMA before and 4 and 12 months after risdiplam treatment initiation using the Sydney Swallow Questionnaire (SSQ) and the bulbar subscore of the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (b-ALSFRS-R). Extremity function was assessed using the Hammersmith Functional Motor Scale Expanded (HFMSE) and Revised Upper Limb Module (RULM).ResultsSubjective swallowing quality, measured with the SSQ, improved after 12 months of therapy with risdiplam. For the b-ALSFRS-R, a non-significant trend towards improvement was observed. The RULM score improved after 12 months of risdiplam therapy, but not the HFMSE score. HFMSE and RULM scores did not correlate with the SSQ but the b-ALSFRS-R score at baseline.ConclusionsThe improvement in subjective swallowing quality under risdiplam treatment, despite an advanced disease stage with severe motor deficits, strengthens the importance of a standardized bulbar assessment in addition to established motor scores. This may reveal relevant treatment effects and help individualize treatment decisions in the future.

Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling

BackgroundThere is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days.MethodsWe recruited patients with respiratory failure at the First People’s Hospital of Yancheng and the People’s Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness.ResultsThe LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value.ConclusionsA nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

A Bayesian approach for investigating the pharmacogenetics of combination antiretroviral therapy in people with HIV.

Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals' treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women's Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.

The relationship between high ratios of CD4/FOXP3 and CD8/CD163 and the improved survivability of metastatic triple-negative breast cancer patients: a multicenter cohort study

BackgroundTriple-negative breast cancer (TNBC) has been documented as the most aggressive subtype of breast cancer. This study aimed to analyze antitumor and protumor immune activities, and their ratios as significant prognostic biomarkers in metastatic TNBC (mTNBC).MethodsA multicenter cohort study was conducted among 103 de novo mTNBC patients. The expression of CD8 and CD163 was evaluated using immunohistochemistry staining, CD4 and FOXP3 using double-staining immunohistochemistry, and PD-L1 using immunohistochemistry and RT-PCR.ResultsMultivariate analysis revealed that high CD4/FOXP3 (HR 1.857; 95% CI 1.049–3.288; p = 0.034) and the CD8/CD163 ratio (HR 2.089; 95% CI 1.174–3.717; p = 0.012) yield significantly improved 1 year overall survival (OS). Kaplan–Meier analysis showed that high levels of CD4 (p = 0.023), CD8 (p = 0.043), CD4/FOXP3 (p = 0.016), CD8/FOXP3 (p = 0.005), CD8/CD163 (p = 0.005) ratios were significantly associated with higher rate of 1 year OS. Furthermore, 1 year OS was directly correlated with antitumor CD4 (R = 0.233; p = 0.018) and CD8 (R = 0.219; p = 0.026) and was indirectly correlated with protumor CD163 and FOXP3 through CD4/FOXP3 (R = 0.282; p = 0.006), CD4/CD163 (R = 0.239; p = 0.015), CD8/FOXP3 (R = 0.260; p = 0.008), and CD8/CD163 (R = 0.258; p = 0.009).ConclusionThis is the first study to demonstrate that high levels of CD4/FOXP3 and CD8/CD163 significantly improved the 1 year OS in de novo mTNBC patients. Thus, we recommend the application of these markers as prognosis determination and individual treatment decision.

Development and validation of a tumor tissue based multivariate biomarker for predicting angiogenesis inhibitor clinical benefit in renal cell carcinoma (RCC).

467 Background: Angiogenesis inhibitors, including vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitors (TKIs), are standard of care therapy (alone and in combination) for several advanced cancers, including RCC. There is an unmet need for a biomarker to identify patients with RCC most likely to benefit from VEGFR TKIs to guide individualized treatment decision making. Hence, we sought to develop and validate a biomarker for predicting single-agent systemic VEGFR TKI benefit in patients with RCC. Methods: Candidate biomarkers were selected by co-expression patterns in VEGFR TKI sensitive/resistant tumor types from pan-solid tumor TCGA expression profiling data and the literature. The angiogenesis inhibitor treatment response score (Angio TRS) was developed as a multivariate expression-based algorithm from RNA-based quantitative transcriptional profiling (qTP) performed in parallel with clinical FFPE tumor based comprehensive genomic profiling, with the Angio TRS High/Low threshold set at the median of clear cell RCC samples. The locked Angio TRS (and High/Low status) was then validated in a cohort of adult RCC patients treated with a systemic line of single agent VEGFR TKI therapy within a prospective observational trial; group outcomes (by time to next therapy [TTNT]) were compared by univariate analysis, Cox proportional hazards modeling (adjusting for age, biologic sex, histology, nuclear grade [incorporating sarcomatoid features], therapy line, and TKI type), and unadjusted Kaplan Meier analysis. Laboratory information for IMDC risk group status was not available. Results: Across 3,721 solid tumor tissue samples, median Angio TRS was highest in known VEGFR TKI sensitive tumors (RCC, thyroid carcinoma, sarcoma). Angio TRS was then validated in a separate cohort of 86 patients with RCC treated with single agent systemic VEGFR TKI (median follow-up 29.2 months; 76% male, 77% clear cell, 14% with sarcomatoid features, 84% 1st line, and 71% treated with sunitinib, pazopanib or axitinib); 52% of patients were Angio TRS High. By univariate analysis, Angio TRS status (p=0.008) and nuclear grade 4 (with or without sarcomatoid features; p=0.06 &amp; 0.07) were most significantly associated with VEGFR TKI TTNT; by multivariate analysis, only Angio TRS status was significantly associated with TTNT (High vs. Low median TTNT 15.8 vs. 5.6 months, adjusted hazard ratio 0.46, p=0.012). Conclusions: Angio TRS is a multivariate expression-based algorithm performed on FFPE tumor tissue, validated to be prognostic of clinical outcome for patients with RCC treated with single agent VEGFR TKI when controlling for clinical factors. Angio TRS may support individualized treatment decision making in patients with advanced RCC. Additional independent validation in a cohort of Kaiser Permanente patients will be presented. Clinical trial information: NCT03061305 .

Circulating Cell-Free SHOX2 DNA Methylation Is a Predictive, Prognostic, and Monitoring Biomarker in Adjuvant and Palliative Anti-PD-1-Treated Melanoma.

The majority of metastatic melanoma patients initially do not respond or acquire resistance to anti-programmed cell death 1 (PD-1) immunotherapy. Liquid biopsy biomarkers might provide useful early response information and allow for personalized treatment decisions. We prospectively assessed circulating cell-free SHOX2 DNA methylation (SHOX2 ccfDNAm) levels and their dynamic changes in blood plasma of melanoma patients by quantitative methylation-specific polymerase chain reaction. Patients were treated with either palliative (n = 42) or adjuvant (n = 55) anti-PD-1 immunotherapy. Moreover, we included n = 126 control patients without evidence of malignant disease. We analyzed SHOX2 ccfDNAm status prior to and 4 weeks after palliative treatment initiation with regard to outcome [objective response, progression-free survival (PFS), and overall survival (OS)]. In the adjuvant setting, we associated longitudinal SHOX2 ccfDNAm status with disease recurrence. Sensitivity was 60% with 25/42 melanoma patients showing increased SHOX2 ccfDNAm levels, whereas specificity was 98% with 123/126 (P < 0.001) control patients having SHOX2 ccfDNAm levels below cut-off. Pretreatment SHOX2 ccfDNAm status did not correlate with outcome; however, SHOX2 ccfDNAm negativity 4 weeks after palliative treatment initiation was strongly associated with improved survival [PFS: hazard ratio (HR) = 0.25, P = 0.002; OS: HR = 0.12, P = 0.007]. Pretreatment positive patients who reached SHOX2 ccfDNAm clearance after 4 weeks of immunotherapy showed an exceptionally beneficial outcome. SHOX2 ccfDNAm testing allowed for an early detection of distant metastases in adjuvant-treated melanoma patients. Our study suggests SHOX2 ccfDNAm to be an early predictor of outcome in anti-PD-1 treated melanoma patients. SHOX2 ccfDNAm testing may aid individualized treatment decision-making.