Three-tier Classification Research Articles

Genome Sequencing Enhances the Diagnostic Yield and Expands the Genetic Landscape of Male Breast Cancer

PurposeTo understand the broader genetic landscape of Male Breast Cancer (MBC), focusing on the utility of genome sequencing (GS) beyond BRCA1/2 (HGNC: 1100, 1101) variants. Methods24 MBC patients underwent a multi-step genetic analysis. Initial screening targeted BRCA1/2 variants followed by GS to identify pathogenic/likely pathogenic (P/LP) germline variants through a three-tiered classification. Polygenic risk score (PRS) analysis was further incorporated using a model for female breast cancer with 2666 non-cancer controls. Exome sequencing (ES) was employed to transition from germline to somatic investigations, assessing second-hit variant and mutational signatures. ResultsThe GS analysis unveiled previously unrecognized P/LP germline variants in BARD1, ATR, BRIP1, and CHEK2 (HGNC: 952, 882, 20473, 16627) among 21 BRCA1/2-negative MBC patients, elevating the diagnostic yield from 12.5% to 33.0% in all MBC. Elevated average PRS was noted compared to controls, with a significant correlation to early-onset MBC when combined with high-penetrance germline pathogenic variants (P = 1.10×10-4). ES analysis further identified significant somatic oncogenic drivers and revealed a dominant mutational signature SBS3 across BRCA1/2-negative samples, reinforcing the contribution of HRD underlying the MBC development. ConclusionOur findings extended the MBC genetic spectrum beyond BRCA1/2 and highlighted the intricate interplay of monogenic and polygenic predispositions, presenting a comprehensive MBC genomic profile.

Multiparametric MRI Radiomics With Machine Learning for Differentiating HER2-Zero, -Low, and -Positive Breast Cancer: Model Development, Testing, and Interpretability Analysis.

BACKGROUND: MRI radiomics has been explored for three-tiered classification of breast cancer HER2 expression (i.e., HER2-zero, HER2-low, or HER2-positive), although understanding of how such models reach their predictions is lacking. OBJECTIVE: To develop and test multiparametric MRI radiomics machine-learning models for differentiating three-tiered HER2 expression levels in patients with breast cancer, and to explain the contributions of model features through local and global interpretations using SHapley Additive exPlanation (SHAP) analysis. METHODS: This retrospective study included 737 patients (mean age, 54.1±10.6 years) with breast cancer from two centers (center 1: n=578; center 2: n=159), who underwent breast MRI and had HER2 expression determined after excisional biopsy. Analysis entailed two tasks: differentiating HER2-negative (i.e., HER2-zero or HER2-low) from HER2-positive tumors (task 1), and differentiating HER2-zero from HER2-low tumors (task 2). For each task, patients from center 1 were randomly assigned in 7:3 ratio to training (task 1: n=405; task 2: n=284) or internal test (task 1: n=173; task 2: n=122) sets; those from center 2 formed an external test set (task 1: n=159; task 2: n=105). Radiomics features were extracted from early-phase dynamic contrast-enhanced images (DCE), T2-weighted images (T2WI), and DWI. For each task, a support vector machine (SVM) was used for feature selection; a multiparametric radiomics score (radscore) was computed using feature weights from SVM correlation coefficients; conventional MRI and combined models were constructed; and model performances were evaluated. SHAP analysis was used to provide local and global interpretations for model outputs. RESULTS: In the external test set, for task 1, AUCs for the conventional MRI model, radscore, and combined model were 0.624, 0.757, and 0.762, respectively; for task 2, AUC for radscore was 0.754, and no conventional MRI model or combined model could be constructed. SHAP analysis identified early-phase DCE features as having the strongest influence for both tasks; T2WI features also had a prominent role for task 2. CONCLUSION: The findings indicate suboptimal performance of MRI radiomics models for noninvasive characterization of HER2 expression. CLINICAL IMPACT: The study provides an example of the use of SHAP interpretation analysis to better understand predictions of imaging-based machine learning models.

The Importance of Being Grade 3: A Plea for a Three-tier Hybrid Classification System for Grade in Primary Non–muscle-invasive Bladder Cancer

Grade is an important determinant of progression in non–muscle-invasive bladder cancer. Although the World Health Organization (WHO) 2004/2016 grading system is recommended, other systems such as WHO1973 and WHO1999 are still widely used. Recently, a hybrid (three-tier) system was proposed, separating WHO2004/2016 high grade (HG) into HG/grade 2 (G2) and HG/G3 while maintaining low grade. We assessed the prognostic performance of HG/G3 and HG/G2. Three independent cohorts with 9712 primary (first diagnosis) Ta-T1 bladder tumors were analyzed. Time to progression was analyzed with cumulative incidence functions and Cox regression models. Harrell’s C-index was used to assess discrimination. Time to progression was significantly shorter for HG/G3 than for HG/G2 in multivariable analyses (cohort 1: hazard ratio [HR] = 1.92; cohort 2: HR = 2.51, and cohort 3: HR = 1.69). Corresponding progression risks at 5 yr were 18%, 20%, and 18% for HG/G3 versus 7.3%, 7.5%, and 9.3% for HG/G2, respectively. Cox models using hybrid grade performed better than models with WHO2004/2016 (all cohorts; p < 0.001). For the three cohorts, C-indices for WHO2004/2016 were 0.69, 0.62, and 0.75, while, for hybrid grade, C-indices were 0.74, 0.68, and 0.78, respectively. Subdividing the HG category into HG/G2 and HG/G3 stratifies time to progression and supports the recommendation to adopt the hybrid grading system for Ta/T1 bladder cancers.

Morus sp. cultivation in Natura 2000 sites: environmental constraints and considerations

Morus sp. is a versatile resource with diverse benefits; however, its cultivation may entail negative environmental impacts. Environmental legislation imposes constraints to mitigate these effects. The current study presents a three-tier classification approach to assess the severity of constraints imposed by environmental legislation on Morus sp. cultivation. The approach is tested in a case study area located in BG0000254 Besaparski vazvishenita, Bratsigovo Municipality, Bulgaria. Approximately 23.6% of this area is under strict protection, thereby prohibiting cultivation. In about 21.3% of the area, Morus sp. cultivation is feasible but most probably an Appropriate Assessment procedure will be required to ensure nonsignificant negative impact. In the remaining 56.4% of the area, notification/screening is necessary, with the likelihood of not requiring a full Appropriate Assessment procedure. Thus, despite a significant portion being under strict protection, there remains potential for Morus sp. cultivation within the study area, albeit with varying procedural complexities. Th&amp;#1077; proposed constraints classification approach can inform land use planning, facilitating informed decision-making in site selection not only for Morus sp. but for all types of permanent crops.

Policy Analysis of the Financial Sector in the Context of Covid-19 the Case of Italy

This paper analyses in detail the financial policies adopted by the national financial sector in the context of the covid-19, the study assesses the number and nature of economic policies introduced during 2020 in 10 countries using data from the Kaggle database. And finally, Italy was selected for the specific study, which introduced the greatest number of policies during the year. The study describes the data through tools such as bar charts, pie charts and tables, and employs a three-tiered classification of policies to give more detail to the analysis. The report shows that Italy's primary focus was on the banking sector with a clear emphasis on support for borrowers. In addition, the Italian government's expansionary fiscal policies have played a key role in revitalizing sectors hit hard by the pandemic, including tourism. While these policies improved Italy's economic situation during the pandemic to some extent, the potential impacts, such as an increase in the national deficit and the burden on the Government, merit future study.

Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer.

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Fluorescein videoangiography (FL-VAG) as a predictor of cerebral bypass patency

BackgroundThe fluorescein videoangiography (FL-VAG) has become a valuable adjunct tool in vascular neurosurgery. This work describes using the FL-VAG during bypass surgery and proposes a classification method for evaluating surgical results. MethodsWe analyzed 26 patients with 50 cerebral bypasses from September 2018 to September 2022. We used a three grades classification method based on the pass of intravenous fluorescein through the anastomosis. Grade 1 represents the synchronous and total filling of the "T" shape ("green T″) formed by the donor and recipient vessel, Grade 2, the asynchronous filling of the anastomosis (incomplete/asynchronous "green T″), and Grade 3, a non-patent anastomosis (absence of "green T″). ResultsOf the 26 patients, 8 underwent one bypass, 14 underwent double bypass, 2 underwent three bypasses, and 2 underwent four bypasses in two different interventions. The type of bypass was end-to-side anastomosis in 47 (94%) cases, internal maxillary artery to middle cerebral artery bypass with a radial artery graft (IMax-MCA anastomosis) in 2 (4%), and PICA-VA transposition in one (2%). We made 24 (48%) bypasses on the right side and 26 (52%) on the left side. After the initial surgery, thirty-nine (78%) bypasses were considered as Grade 1, 5 (10%) as Grade 2, and 6 (12%) as Grade 3. After intraoperative bypass patency assessment (IBPA), 45 (90%) of the bypasses were considered Grade 1 and remained patent on CTA. ConclusionsUsing FL-VAG and a three-tier classification method is a reliable tool to predict bypass patency. It is safe, low-risk, and available worldwide.

Multi-modal ultrasound multistage classification of PTC cervical lymph node metastasis via DualSwinThyroid.

This study aims to predict cervical lymph node metastasis in papillary thyroid carcinoma (PTC) patients with high accuracy. To achieve this, we introduce a novel deep learning model, DualSwinThyroid, leveraging multi-modal ultrasound imaging data for prediction. We assembled a substantial dataset consisting of 3652 multi-modal ultrasound images from 299 PTC patients in this retrospective study. The newly developed DualSwinThyroid model integrates various ultrasound modalities and clinical data. Following its creation, we rigorously assessed the model's performance against a separate testing set, comparing it with established machine learning models and previous deep learning approaches. Demonstrating remarkable precision, DualSwinThyroid achieved an AUC of 0.924 and an 96.3% accuracy on the test set. The model efficiently processed multi-modal data, pinpointing features indicative of lymph node metastasis in thyroid nodule ultrasound images. It offers a three-tier classification that aligns each level with a specific surgical strategy for PTC treatment. DualSwinThyroid, a deep learning model designed with multi-modal ultrasound radiomics, effectively estimates the degree of cervical lymph node metastasis in PTC patients. In addition, it also provides early, precise identification and facilitation of interventions for high-risk groups, thereby enhancing the strategic selection of surgical approaches in managing PTC patients.

Clinicopathological Correlation of Polyomavirus Nephropathy in Renal Allograft Recipients According to the Banff 2018 Classification.

Polyomavirus nephropathy (PVN) is a rare kidney disease caused by the BK virus, a strain of polyomavirus. The disease primarily affects transplant recipients, which is related to intensive immunosuppression protocol and can lead to kidney allograft failure. The objective of this study is to analyze histopathological features of PVN using the Banff 2018 PVN classification and to determine clinical features and outcomes of patients with PVN in each histologic class. The study included 44 patients who had been diagnosed with PVNby renal allograft biopsy in a large tertiary care hospital in Thailand from January 2011 to January 2020. The kidney biopsy slides were reviewed for Banff 2018 PVN classification and other histologic features. Patient demographic information, clinical data, and laboratory results were retrospectively collected. Nine (20.45%), 27 (61.36%), and eight (18.18%) cases of PVN were Class I, Class II, and Class III, respectively. The time from transplant to PVN diagnosis for Classes I, II, and III was four, 19, and 33.5months, respectively. Class III had the worst clinical outcomes in terms of deterioration of allograft function, the lowest rate of resolution, and the highest rate of graft failure. PVN classification provides prognostic information in renal allograft biopsy. Our study confirmed the validity of the three-tier histologic PVN classification put forward by the Banff Working Group in 2018.

Multi-RAT-enabled edge computing for vehicle-to-everything architectures

The accelerating deployment of vehicular networks in smart cities sparked a demand for a wider diversity of on-board applications, extending their performance requirements. Higher computation needs and stricter delay constraints provide new challenges for edge computing vehicular architectures, especially regarding urgent data and high-priority tasks. In that regard, QoS-provisioning appears as imperative, along with optimized resource requesting at RSUs. To this end, we propose CAVTOMEC, a multi-RAT location-aware, context-aware task offloading solution with QoS provisioning for MEC vehicular networks. Three concurrent mechanisms are at play: traffic classification, CAM-beacon-enhanced location awareness and long-range V2N resource polling. Three-tier traffic classification identifies task priority, while location and resource awareness participate in the selection of the most appropriate offloading destination depending on these priorities. The resource awareness mechanism is developed in two phases: immediate available resources and prospective resources based on edge server queue state. Experimental results corroborate the gains of our scheme compared to a standard offloading scheme, especially for high-priority tasks, with success rates increased by up to 14%, and offloading delays reduced by up to 24%.

Superior mesenteric artery clinical classification and morphometrical analysis.

The superior mesenteric artery is one of the most important arteries in the abdominal cavity, which is of great clinical importance, especially in surgical procedures and fatal ischemic complications. The aim of this study was to develop a clinical classification of the superior mesenteric artery. Postmortem contrast-enhanced computed tomography of 104 (29.8% female, age 50.7±18.7) human bodies were analyzed. Based on anatomic predisposition to ischemic and iatrogenic complications, a three-tiered clinical classification of the superior mesenteric artery was developed. Type 0 was defined as standard risk for ischemic and iatrogenic complications. Type 1 was defined as increased thromboembolic risk with decreased risk of iatrogenic bleeding, and type 2 was defined as decreased ischemic risk with increased risk of iatrogenic bleeding. The supply area of the superior mesenteric artery was divided into 4 regions: pancreas, caecum, ascending colon, and transverse colon. Type 0 (standard risk) was found in 62.5% of cases. Type 1 was most frequently observed in the ascending colon region (15.4%). Type 2 was most frequently observed in the pancreatic region (17.3%). Regarding type, most abnormalities were found in the region of the ascending colon (18.3%), pancreas region (17.3%), and transverse colon (16.3%). The proposed clinical classification of SMA links anatomic variations in morphology with their clinical significance. A simple, three-level classification can be easily applied in daily practice and serve as a great support for preoperative evaluation and recognition of patients at risk of iatrogenic or thromboembolic complications.

Way leaves, rights of way and ducting policies for 5G mobile network deployment in India using international best practices

PurposeThis paper aims to draw lessons for telecommunications (telecom) network deployment in India, from a study of policy and regulatory approaches taken by other federal administrations in streamlining processes for site clearances, grant of rights of way (RoW) and approvals for local infrastructure deployment and sharing. With the urgent need for setting up small cells and rapid fiberisation of networks in the 5G era, the importance of such processes has gained prominence.Design/methodology/approachThe authors adopt qualitative thematic content analysis with three-tier coding and classification to identify themes in archival and current documentary data and information obtained from subject-matter experts in the countries studied.FindingsFormulation and implementation of telecom policy is led by national governments. However, national telecom administrations, in recognition of new needs, have co-opted states and local authorities as partners in development of telecom networks, providing the overall framework, guidance and appropriate incentives where required.Practical implicationsThis cooperative model could work well in India, where telecom policy making and regulation is the prerogative of the central government, but administration of RoW and local clearances for cable laying, tower siting and associated infrastructure activities for expanding telecom networks are left to decentralised decision-making in the states and local bodies.Originality/valueThis research attempts to sytematise, thematise and draw cross-country comparisons to inform regulatory and administrative policy for 5G infrastructure rollout in India.

Quantification and significance of extraprostatic findings on prostate MRI: a retrospective analysis and three-tier classification

ObjectivesTo quantify extraprostatic findings (EPFs) on prostate MRI, estimate the proportion of reported and unreported EPFs, assess their clinical importance, and propose standardized reporting of EPFs.Materials and methodsProstate 3-T MRI studies, reports, and clinical data from 623 patients (age 67.9 ± 8.2 years) were retrospectively analyzed and re-evaluated for the presence of EPFs and their clinical significance: E1—no finding or findings that have no clinical significance; E2—potentially significant findings; and E3—significant findings.ResultsSecondary reading identified 1236 EPFs in 593 patients (1.98 ± 1.13 EPFs per patient, no EPFs in 30 patients), from which 468 (37.8%) were mentioned in the original report. The most common findings included diverticulosis (44% of patients), hydrocele (34%), inguinal fat hernia (16%), and bladder wall trabecular hypertrophy (15%). There were 80 (6.5%) E2 EPFs and 30 (2.4%) E3 EPFs. From E3 EPFs, 10 (33%) were not originally reported. A workup was suggested in 35 (52%) of the 67 originally reported E2 and E3 findings with follow-up and performed in 20 (30%). Fourteen (21%) EPFs in 11 patients influenced their management. Four experienced radiologists originally reported 1.8 to 2.5 findings per patient (p < 0.0001).ConclusionsEPFs on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. Only 30% of E2 and E3 findings are further explored, and 21% influence patient management. We suggest that an “E” category should be attached to the PI-RADS system to identify the presence of EPFs that require further workup.Critical relevance statementExtraprostatic findings on prostate MRI are frequent, but only 2.4% are clinically significant (E3), and 33% of these are not reported. We advocate standardized reporting of extraprostatic findings indicating their clinical significance.Key points• Extraprostatic findings on prostate MRI are frequent with an average of two findings per patient.• 2.4% of extraprostatic findings are significant, and 33% of these are not reported.• There is a significant variability among experienced radiologists in reporting extraprostatic findings.Graphical

Clinical-Genomic Risk Group Classification of Suspicious Lesions on Prostate Multiparametric-MRI.

The utilization of multi-parametric MRI (mpMRI) in clinical decisions regarding prostate cancer patients' management has recently increased. After biopsy, clinicians can assess risk using National Comprehensive Cancer Network (NCCN) risk stratification schema and commercially available genomic classifiers, such as Decipher. We built radiomics-based models to predict lesions/patients at low risk prior to biopsy based on an established three-tier clinical-genomic classification system. Radiomic features were extracted from regions of positive biopsies and Normally Appearing Tissues (NAT) on T2-weighted and Diffusion-weighted Imaging. Using only clinical information available prior to biopsy, five models for predicting low-risk lesions/patients were evaluated, based on: 1: Clinical variables; 2: Lesion-based radiomic features; 3: Lesion and NAT radiomics; 4: Clinical and lesion-based radiomics; and 5: Clinical, lesion and NAT radiomic features. Eighty-three mpMRI exams from 78 men were analyzed. Models 1 and 2 performed similarly (Area under the receiver operating characteristic curve were 0.835 and 0.838, respectively), but radiomics significantly improved the lesion-based performance of the model in a subset analysis of patients with a negative Digital Rectal Exam (DRE). Adding normal tissue radiomics significantly improved the performance in all cases. Similar patterns were observed on patient-level models. To the best of our knowledge, this is the first study to demonstrate that machine learning radiomics-based models can predict patients' risk using combined clinical-genomic classification.

Clinical profile and outcomes of childhood dilated cardiomyopathy - A single-center three-decade experience.

Dilated cardiomyopathy (DCM) is an important cause of heart failure (HF) among children. Research on pediatric DCM remains surprisingly scarce. The primary objective of the study was to evaluate the clinical profile and outcomes of pediatric DCM and the secondary objective was to study the predictors of outcome. We enrolled all patients with cardiomyopathy who presented to us between 1990 and 2020 and were younger than 18 years. During the 30-year study period, we identified 233 cases of pediatric cardiomyopathy. One hundred and nineteen (51%) cases had DCM. This retrospective cohort was analyzed to study their outcome and the possible predictors of outcome. Nearly, 8% presented in the neonatal period, and 37% in infancy. The most common mode of presentation was dyspnea on exertion (71%). Ninety-three patients presented in heart failure (78%). The median left ventricular dimension z-score in diastole was 4.3 (range 2.5-9.06). The median left ventricle (LV) ejection fraction was 31%. Seventy-two percent of this cohort were on angiotensin-converting-enzyme inhibitors, 40% on aldosterone antagonists, and 47% on beta-blockers. One-third had syndromic, metabolic, genetic, or any secondary cause identified. Twenty-seven patients satisfied the three-tiered clinical classification for the diagnosis of probable acute myocarditis. Over a mean follow-up of 3.29 years, 27% were lost to follow-up. Among the remaining patients who were on follow-up (n = 86), 39 (45%) died, 31 (36%) recovered, and 16 (18%) had persistent LV dysfunction. Heart Failure was the most common cause of death. Eight patients in this cohort (4.2%) had thromboembolic phenomena. Nine had sustained ventricular arrhythmias and six had atrial/junctional arrhythmias. Among the various risk factors studied, only infantile onset had a significant relationship with death or ventricular arrhythmias (P value- 0.05). The 5-year survival rate of DCM patients was 59%. A reasonably good percentage of our population showed recovery of the left ventricular function (36%). Only infantile onset had a significant relationship with death or ventricular arrhythmias. The outcome in our DCM cohort is similar to other population cohorts.

Human papillomavirus and p53 status define three types of vulvar squamous cell carcinomas with distinct clinical, pathological, and prognostic features.

Based on their etiological relationship with human papillomavirus (HPV), the 2020 WHO classification has divided vulvar squamous cell carcinomas (VSCC) into two distinct types, HPV-associated and HPV-independent, and HPV-independent tumours have recently been divided according to p53 status. Nevertheless, the clinical and prognostic significance of this classification has not been clearly established. We analysed the differential clinical, pathological, and behavioural characteristics of these three types of VSCC in a large series of patients. VSCC samples from patients who underwent primary surgery at the Hospital Clinic of Barcelona, Spain, during a 47-year period (January 1975 to January 2022) were analysed (n = 190). HPV detection, p16, and p53 immunohistochemical staining were evaluated. We also analysed recurrence-free survival (RFS) and disease-specific survival (DSS). Thirty-three tumours (17.4%) were HPV-associated and 157 (82.6%) HPV-independent. Of these, 20 showed normal and 137 abnormal p53 expression. The two types of HPV-independent tumours showed worse RFS in the multivariate analysis (hazard ratio [HR] = 3.63; P = 0.023 for the HPV-independent p53 normal VSCC and HR = 2.78; P = 0.028 for the HPV-independent p53 abnormal VSCC). Although the differences were not significant, HPV-independent VSCC had worse DSS than HPV-associated VSCC. Although patients with HPV-independent p53 normal tumours had worse RFS than patients with HPV-independent p53 abnormal tumours, the DSS was better for the former group. Only advanced FIGO stage was associated with worse DSS in multivariate analysis (HR = 2.83; P = 0.010). The association of HPV and p53 status have prognostic implications, reinforcing a three-tier molecular classification of VSCC (HPV-associated VSCC, HPV-independent VSCC with normal p53, HPV-independent VSCC with abnormal p53).

Personalized radiotherapy of brain metastases: survival prediction by means of dichotomized or differentiated blood test results?

The validated LabBM score (laboratory parameters in patients with brain metastases) represents a widely applicable survival prediction model, which incorporates 5 blood test results (serum lactate dehydrogenase (LDH), C-reactive protein (CRP), albumin, platelets and hemoglobin). All tests are classified as normal or abnormal, without accounting for the wide range of abnormality observed in practice. We tested the hypothesis that improved stratification might be possible, if more granular test results are employed. Retrospective analysis of 198 patients managed with primary whole-brain radiotherapy in one of the institutions who validated the original LabBM score. For two blood tests (albumin, CRP), discrimination was best for the original dichotomized version (normal/abnormal). For two others (LDH, hemoglobin), a three-tiered classification was best. The number of patients with low platelet count was not large enough for detailed analyses. A modified LabBM score was developed, which separates the intermediate of originally 3 prognostic groups into 2 statistically significantly different strata, resulting in a 4-tiered score. This initial proof-of-principle study suggests that granular blood test results might contribute to further improvement of the score, or alternatively development of a nomogram, if additional large-scale studies confirm the encouraging results of the present analysis.

Postpartum hemorrhage risk assessment—revisiting the three-tiered classification of low- medium- and high-risk

ACOG promulgates classification of individuals admitted for delivery into three-tiers for postpartum hemorrhage (PPH): Low-, Medium-, or High-Risk. We sought to report the frequency of each tier and the clinical outcomes linked to each one. Over 12 consecutive months, charts of all singletons who delivered at a Level IV maternal center were reviewed. Multiple gestations and those transferred after delivery were excluded. Using pre-defined criteria, chart abstraction was done by qualified medical personnel, and data quality was audited on 10% of the charts by staff not involved in data abstraction. Using the California Maternal Quality Care Collaborative (CMQCC) schema, with the known risk factors until delivery, all singletons were categorized into three tiers. PPH was defined as estimated or quantitative blood loss ≥1,000 mL. We contrasted risks of composite maternal morbidity (CMM) among all classification groups, with a relative risk (RR) with a 95% confidence interval (CI) as the effect measure calculated using Poisson regression adjusted for potential confounders. Of the 4,516 singletons, 1,854 (41%) were categorized as being low-risk, 1,239 (27%) were medium-, and 1423 (32%) as high-risk. The risk factors for the 3 groups are in Table 1. The risk of CMM was 19% higher in the medium versus low-risk groups (RR 1.19, 95% CI 1.03-1.36) and 16% higher comparing high versus low-risk groups (RR 1.16, 95% CI 1.02-1.32), but not amongst the medium vs. high-risk groups (aRR 0.98, 95% CI 0.85-1.12). Blood loss of at least 1,000 mL and transfusion followed a similar pattern to the CMM (Table 2). The three maternal deaths were not directly related to PPH. At our Level IV maternal center, the three-tiered postpartum hemorrhage classification performed poorly since over 1 out 5 “low-risk” had hemorrhagic sequelae and no difference in outcomes was noted between medium versus high-risk groups. A better predictive model is needed for PPH and associated morbidity.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

Most traditional digital forensic techniques identify irrelevant files in a corpus using keyword search, frequent hashes, frequent paths, and frequent size methods. These methods are based on Message Digest and Secure Hash Algorithm-1, which result in a hash collision. The threshold criteria of files based on frequent sizes will lead to imprecise threshold values that result in an increased evaluation of irrelevant files. The blacklisted keywords used in forensic search are based on literal and non-lexical, thus resulting in increased false-positive search results and failure to disambiguate unstructured text. Due to this, many extraneous files are also being considered for further investigations, exacerbating the time lag. Moreover, the non-availability of standardized forensic labeled data results in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(O(2^{n}))$ </tex-math></inline-formula> time complexity during the file classification process. This research proposes a three-tier Keyword Metadata Pattern framework to overcome these significant concerns. Initially, Secure Hash algorithm-256 hash for the entire corpus is constructed along with custom regex and stop-words module to overcome hash collision, imprecise threshold values, and eliminate recurrent files. Then blacklisted keywords are constructed by identifying vectorized words that have proximity to overcome traditional keyword search’s drawbacks and to overcome false positive results. Dynamic forensic relevant patterns based on massive password datasets are designed to search for unique, relevant patterns to identify the significant files and overcome the time lag. Based on tier-2 results, files are preliminarily classified automatically in O(log n) complexity, and the system is trained with a machine learning model. Finally, when experimentally evaluated, the overall proposed system was found to be very effective, outperforming the existing two-tier model in terms of finding relevant files by automated labeling and classification in O(nlog n) complexity. Our proposed model could eliminate 223K irrelevant files and reduce the corpus by 4.1% in tier-1, identify 16.06% of sensitive files in tier-2, and classify files with 91% precision, 95% sensitivity, 91% accuracy, and 0.11% Hamming loss compared to the two-tier system.

Three-tiered EGFr domain risk stratification for individualized NOTCH3-small vessel disease prediction.

Cysteine-altering missense variants (NOTCH3cys) in one of the 34 epidermal growth-factor-like repeat (EGFr) domains of the NOTCH3 protein are the cause of NOTCH3-associated small vessel disease (NOTCH3-SVD). NOTCH3-SVD is highly variable, ranging from cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) at the severe end of the spectrum to non-penetrance. The strongest known NOTCH3-SVD modifier is NOTCH3cys variant position: NOTCH3cys variants located in EGFr domains 1-6 are associated with a more severe phenotype than NOTCH3cys variants located in EGFr domains 7-34. The objective of this study was to further improve NOTCH3-SVD genotype-based risk prediction by using relative differences in NOTCH3cys variant frequencies between large CADASIL and population cohorts as a starting point. Scientific CADASIL literature, cohorts and population databases were queried for NOTCH3cys variants. For each EGFr domain, the relative difference in NOTCH3cys variant frequency (NVFOR) was calculated using genotypes of 2574 CADASIL patients and 1647 individuals from population databases. Based on NVFOR cut-off values, EGFr domains were classified as either low (LR-EGFr), medium (MR-EGFr) or high risk (HR-EGFr). The clinical relevance of this new three-tiered EGFr risk classification was cross-sectionally validated by comparing SVD imaging markers and clinical outcomes between EGFr risk categories using a genotype-phenotype data set of 434 CADASIL patients and 1003 NOTCH3cys positive community-dwelling individuals. CADASIL patients and community-dwelling individuals harboured 379 unique NOTCH3cys variants. Nine EGFr domains were classified as an HR-EGFr, which included EGFr domains 1-6, but additionally also EGFr domains 8, 11 and 26. Ten EGFr domains were classified as MR-EGFr and 11 as LR-EGFr. In the population genotype-phenotype data set, HR-EGFr individuals had the highest risk of stroke [odds ratio (OR) = 10.81, 95% confidence interval (CI): 5.46-21.37], followed by MR-EGFr individuals (OR = 1.81, 95% CI: 0.84-3.88) and LR-EGFr individuals (OR = 1 [reference]). MR-EGFr individuals had a significantly higher normalized white matter hyperintensity volume (nWMHv; P = 0.005) and peak width of skeletonized mean diffusivity (PSMD; P = 0.035) than LR-EGFr individuals. In the CADASIL genotype-phenotype data set, HR-EGFr domains 8, 11 and 26 patients had a significantly higher risk of stroke (P = 0.002), disability (P = 0.041), nWMHv (P = 1.8 × 10-8), PSMD (P = 2.6 × 10-8) and lacune volume (P = 0.006) than MR-EGFr patients. SVD imaging marker load and clinical outcomes were similar between HR-EGFr 1-6 patients and HR-EGFr 8, 11 and 26 patients. NVFOR was significantly associated with vascular NOTCH3 aggregation load (P = 0.006), but not with NOTCH3 signalling activity (P = 0.88). In conclusion, we identified three clinically distinct NOTCH3-SVD EGFr risk categories based on NFVOR cut-off values, and identified three additional HR-EGFr domains located outside of EGFr domains 1-6. This EGFr risk classification will provide an important key to individualized NOTCH3-SVD disease prediction.