Screening optimal candidates with operable, early-stage triple-negative breast cancer benefitting from capecitabine maintenance: A post-hoc analysis of the SYSUCC-001 study.

BACKGROUND: To explore whether specific clinicopathological covariates are predictive for a benefit from capecitabine maintenance in early-stage triple-negative breast cancer (TNBC) in the SYSUCC-001 phase III clinical trial. METHODS: Candidate covariates included age, menstrual status, type of surgery, postoperative chemotherapy regimen, Ki-67 percentage, histologic grade, primary tumor size, lymphovascular invasion, node status, and capecitabine medication. Their nonlinear effects were modeled by restricted cubic spline. The primary endpoint was disease-free survival (DFS). A survival prediction model was constructed using Cox proportional hazards regression analysis. RESULTS: All 434 participants (306 in development cohort and 128 in validation cohort) were analyzed. The estimated 5-year DFS in development and validation cohorts were 77.8 % (95 % CI, 72.9%-82.7 %) and 78.2 % (95 % CI, 70.9%-85.5 %), respectively. Age and node status had significant nonlinear effects on DFS. The prediction model constructed using four covariates (node status, lymphovascular invasion, capecitabine maintenance, and age) demonstrated satisfactory calibration and fair discrimination ability, with C-index of 0.722 (95 % CI, 0.662-0.781) and 0.764 (95 % CI, 0.668-0.859) in development and validation cohorts, respectively. Moreover, patient classification was conducted according to their risk scores calculated using our model, in which, notable survival benefits were reported in low-risk subpopulations. An easy-to-use online calculator for predicting benefit of capecitabine maintenance was also designed. CONCLUSIONS: The evidence-based prediction model can be readily assessed at baseline, which might help decision making in clinical practice and optimize patient stratification, especially for those with low-risk, capecitabine maintenance might be a potential strategy in the early-disease setting.

Effect of End-Stage Renal Disease Prospective Payment System on Utilization of Peritoneal Dialysis in Patients with Kidney Allograft Failure.

INTRODUCTION: The Center for Medicare and Medicaid Services (CMS) introduced an End Stage Renal Disease (ESRD) Prospective Payment System (PPS) in 2011 to increase the utilization of home dialysis modalities, including peritoneal dialysis (PD). Several studies have shown a significant increase in PD utilization after PPS implementation. However, its impact on patients with kidney allograft failure remains unknown. METHODS: We conducted an interrupted time series (ITS) analysis using data from the United States Renal Data System (USRDS) that include all adult kidney transplant recipients with allograft failure who started dialysis between 2005 and 2019. We compared the PD utilization in the pre-PPS period (2005-2010) to the fully implemented post-PPS period (2014 - 2019) for early (within 90 days) and late (91-365 days) PD experience. RESULTS: 27507 adult recipients with allograft failure started dialysis during the study period. There was no difference in early PD utilization between the pre-PPS and the post-PPS period in either immediate change (0.3% increase; 95%CI: -1.95%, 2.54%; p=0.79) or rate of change over time (0.28% increase per year; 95%CI: -0.16%, 0.72%; p=0.18). Subgroup analyses revealed a trend toward higher PD utilization post-PPS in for-profit and large-volume dialysis units. There was a significant increase in PD utilization in the post-PPS period in units with low PD experience in the pre-PPS period. Similar findings were seen for the late PD experience. CONCLUSION: PPS did not significantly increase the overall utilization of PD in patients initiating dialysis after allograft failure.

Does the Dose of Standard Adjuvant Chemotherapy Affect the Triple-negative Breast Cancer Benefit from Extended Capecitabine Metronomic Therapy? An Exploratory Analysis of the SYSUCC-001 Trial.

Purpose: Results from studies of extended capecitabine after the standard adjuvant chemotherapy in early stage triple-negative breast cancer (TNBC) were inconsistent, and only low-dose capecitabine from the SYSUCC-001 trial improved disease-free survival (DFS). Adjustment of the conventional adjuvant chemotherapy doses affect the prognosis and may affect the efficacy of subsequent treatments. This study investigated whether the survival benefit of the SYSUCC-001 trial was affected by dose adjustment of the standard adjuvant chemotherapy or not. Patients and Methods: We reviewed the adjuvant chemotherapy regimens before the extended capecitabine in the SYSUCC-001 trial. Patients were classified into "consistent" (standard acceptable dose) and "inconsistent" (doses lower than acceptable dose) dose based on the minimum acceptable dose range in the landmark clinical trials. Cox proportional hazards model was used to investigate the impact of dose on the survival outcomes. Results: All 434 patients in SYSUCC-001 trial were enrolled in this study. Most of patients administered the anthracycline-taxane regimen accounted for 88.94%. Among patients in the "inconsistent" dose, 60.8% and 47% received lower doses of anthracycline and taxane separately. In the observation group, the "inconsistent" dose of anthracycline and taxane did not affect DFS compared with the "consistent" dose. Moreover, in the capecitabine group, the "inconsistent" anthracycline dose did not affect DFS compared with the "consistent" dose. However, patients with "consistent" taxane doses benefited significantly from extended capecitabine (P=0.014). The sufficient dose of adjuvant taxane had a positive effect of extended capecitabine (hazard ratio [HR] 2.04; 95% confidence interval [CI] 1.02 to 4.06). Conclusion: This study found the dose reduction of adjuvant taxane might negatively impact the efficacy of capecitabine. Therefore, the reduction of anthracycline dose over paclitaxel should be given priority during conventional adjuvant chemotherapy, if patients need dose reduction and plan for extended capecitabine.

Atrial lead perforation early after device implantation-a case report and literature review.

We report three patients with screw-in lead perforation in the right atrial free wall not long after device implantation. All the patients complained of intermittent stabbing chest pain associated with deep breathing during the implantation. The "dry" epicardial puncture was utilized to avoid hemopericardium during lead extraction in the first case. The atrial electrode was repositioned in all cases and replaced by a new passive fixation lead in two patients with resolution of the pneumothorax or pericardial effusion. A literature review of 50 reported cases of atrial lead perforation was added to the findings in our case report.

Association of Circulating 25-Hydroxyvitamin D and Recurrence of Glomerulonephritis in Kidney Transplant Recipients: The Wisconsin Allograft Recipient Database (WisARD).

Background: Recurrence of glomerulonephritis (GN) is a significant contributor to long-term allograft failure among kidney transplant recipients (KTRs) with kidney failure because of GN. Accumulating evidence has revealed the role of vitamin D in both innate and adaptive immunity. Although vitamin D deficiency is common among KTRs, the association between 25-hydroxyvitamin D (25[OH]D) and GN recurrence in KTRs remains unclear. Methods: We analyzed data from KTRs with kidney failure caused by GN who received a transplant at our center from 2000 to 2019 and had at least 1 valid posttransplant serum 25(OH)D measurement. Survival analyses were performed using a competing risk regression model considering other causes of allograft failure, including death, as competing risk events. Results: A total of 67 cases of GN recurrence were identified in 947 recipients with GN followed for a median of 7.0 y after transplant. Each 1 ng/mL lower serum 25(OH)D was associated with a 4% higher hazard of recurrence (subdistribution hazard ratio [HR]: 1.04; 95% confidence interval [CI], 1.01-1.06). Vitamin D deficiency (≤20 ng/mL) was associated with a 2.99-fold (subdistribution HR: 2.99; 95% CI, 1.56-5.73) higher hazard of recurrence compared with vitamin D sufficiency (≥30 ng/mL). Results were similar after further adjusting for concurrent urine protein-creatinine ratio, serum albumin, and estimated glomerular filtration rate (eGFR). Conclusions: Posttransplant vitamin D deficiency is associated with a higher hazard of GN recurrence in KTRs. Further prospective observational studies and clinical trials are needed to determine any causal role of vitamin D in the recurrence of GN after kidney transplantation. More in vitro and in vivo experiments would be helpful to understand its effects on autoimmune and inflammation processes.

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.

OBJECTIVE: To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT. METHODS: A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR. RESULTS: For signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model. CONCLUSION: The addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

A subpopulation of luminal progenitors secretes pleiotrophin to promote angiogenesis and metastasis in inflammatory breast cancer.

Inflammatory breast cancer (IBC) is a highly aggressive subtype of breast cancer characterized by rapidly arising diffuse erythema and edema. Genomic studies have not identified consistent alterations and mechanisms that differentiate IBC from non-IBC tumors, suggesting that the microenvironment could be a potential driver of IBC phenotypes. Here, using single-cell RNA sequencing, multiplex staining, and serum analysis in IBC patients, we identified enrichment of a subgroup of luminal progenitor (LP) cells containing high expression of the neurotropic cytokine pleiotrophin (PTN) in IBC tumors. PTN secreted by the LP cells promoted angiogenesis by directly interacting with the NRP1 receptor on endothelial tip cells located in both IBC tumors and the affected skin. NRP1 activation in tip cells led to recruitment of immature perivascular cells in the affected skin of IBC, which are correlated with increased angiogenesis and IBC metastasis. Together, these findings reveal a role for crosstalk between LPs, endothelial tip cells, and immature perivascular cells via PTN-NRP1 axis in the pathogenesis of IBC, which could lead to improved strategies for treating IBC.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.

BACKGROUND: Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS: The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS: Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION: BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Pyrotinib Combined with Vinorelbine in Patients with Previously Treated HER2-Positive Metastatic Breast Cancer: A Multicenter, Single-Arm, Prospective Study.

PURPOSE: This study aims to evaluate the efficacy and safety of a new combination treatment of vinorelbine and pyrotinib in human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) and provide higher level evidence for clinical practice. MATERIALS AND METHODS: This was a prospective, single-arm, phase 2 trial conducted at three institutions in China. Patients with HER2-positive MBC, who had previously been treated with trastuzumab plus a taxane or trastuzumab plus pertuzumab combined with a chemotherapeutic agent, were enrolled between March 2020 and December 2021. All patients received pyrotinib 400 mg orally once daily plus vinorelbine 25 mg/m2 intravenously or 60-80 mg/m2 orally on day 1 and day 8 of 21-day cycle. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included the objective response rate (ORR), disease control rate (DCR), overall survival, and safety. RESULTS: A total of 39 patients were enrolled. All patients had been pretreated with trastuzumab and 23.1% (n=9) of them had accepted trastuzumab plus pertuzumab. The median follow-up time was 16.3 months (95% confidence interval [CI], 5.3 to 27.2), and the median PFS was 6.4 months (95% CI, 4.0 to 8.8). The ORR was 43.6% (95% CI, 27.8% to 60.4%) and the DCR was 84.6% (95% CI, 69.5% to 94.1%). The median PFS of patients with versus without prior pertuzumab treatment was 4.6 and 8.3 months (p=0.017). The most common grade 3/4 adverse events were diarrhea (28.2%), neutrophil count decreased (15.4%), white blood cell count decreased (7.7%), vomiting (5.1%), and anemia (2.6%). CONCLUSION: Pyrotinib plus vinorelbine showed promising efficacy and tolerable toxicity as second-line treatment in patients with HER2-positive MBC.

SHC4 orchestrates ß-catenin pathway-mediated metastasis in triple-negative breast cancer by promoting Src kinase autophosphorylation.

Triple-negative breast cancer (TNBC) is highly aggressive and metastatic, and has the poorest prognosis among all breast cancer subtypes. Activated ß-catenin is enriched in TNBC and involved in Wnt signaling-independent metastasis. However, the underlying mechanisms of ß-catenin activation in TNBC remain unknown. Here, we found that SHC4 was upregulated in TNBC and high SHC4 expression was significantly correlated with poor outcomes. Overexpression of SHC4 promoted TNBC aggressiveness in vitro and facilitated TNBC metastasis in vivo. Mechanistically, SHC4 interacted with Src and maintained its autophosphorylated activation, which activated ß-catenin independent of Wnt signaling, and finally upregulated the transcription and expression of its downstream genes CD44 and MMP7. Furthermore, we determined that the PxPPxPxxxPxxP sequence on CH2 domain of SHC4 was critical for SHC4-Src binding and Src kinase activation. Overall, our results revealed the mechanism of ß-catenin activation independent of Wnt signaling in TNBC, which was driven by SHC4-induced Src autophosphorylation, suggesting that SHC4 might be a potential prognostic marker and therapeutic target in TNBC.

Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning.

OBJECTIVE: To construct and validate a multi-dimensional model based on multiple machine leaning algorithms to predict PCLM using multi-parameter magnetic resonance (MRI) sequences with clinical and imaging parameters. METHODS: A total of 148 PDAC retrospectively examined patients were classified as metastatic or non-metastatic based on results at 3 months after surgery. The radiomics features of the primary tumor were extracted from T2WI images, followed by dimension reduction. Then, multiple machine learning methods were used to construct models. Independent predictors were also screened using multifactor logistic regression and a nomogram was constructed in combination with the radiomics model. Area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the accuracy and reliability of the nomogram. RESULTS: The diagnostic efficacy of the radiomics model in the training and test set was 0.822 and 0.803, sensitivity was 0.742 and 0.692, and specificity was 0.792 and 0.875, respectively. The diagnostic efficacy of the nomogram in the training and test set was 0.866 and 0.832. CONCLUSION: A radiomics nomogram based on machine learning improved the accuracy of predicting PCLM and may be useful for early preoperative diagnosis.

"Moderate" adjuvant chemotherapy-induced leukopenia is beneficial for survival of patients with early breast cancer: a retrospective study.

BACKGROUND: The association between chemotherapy-induced leukopenia (CIL) and survival for patients with early breast cancer (EBC) is not known. We investigated the relationship between different grades of CIL and survival in patients with EBC receiving adjuvant chemotherapy. METHODS: A total of 442 patients with EBC receiving a regimen containing an anthracycline (A) and taxane (T) were included into our analysis. Survival analyses were undertaken using Kaplan-Meier curves. The P-value was calculated using the log rank test. Subgroup analysis was conducted to investigate the correlation of CIL grade and survival based on the clinicopathological characteristics of patients. Afterwards, univariate and multivariate analyses screened out independent prognostic factors to construct a prognostic model, the robustness of which was verified. RESULTS: Patients with EBC who experienced grade 2-4 ("moderate" and "severe") CIL were associated with longer overall survival (OS) than those with grade 0-1 (mild) CIL (P = 0.021). Compared with patients with mild CIL, OS was longer in patients with severe CIL (P = 0.029). Patients who suffered from moderate CIL tended to have longer OS than those with mild CIL (P = 0.082). Nevertheless, there was no distinguishable difference in OS between moderate- or severe-CIL groups. Subgroup analysis revealed that patients with moderate CIL had longer OS than those with mild CIL among patients who were premenstrual, or with human epidermal growth factor receptor 2-positive (HER2+), > 3 lymph nodes with metastases, a tumor diameter > 5 cm. A prognostic model based on menstrual status, N stage, and CIL grade showed satisfactory robustness. CONCLUSION: The grade of CIL was strongly associated with the prognosis among patients with EBC who received a regimen containing both anthracyclines and taxanes. Patients with a "moderate" CIL grade tended to have better survival outcomes.

Effect of Lipid Levels on Tumor-Infiltrating Lymphocytes and Prognosis in Patients with Triple-Negative Breast Cancer.

Objective: Dyslipidemia can promote cell proliferation, malignant transformation, metastasis, and cancer recurrence. Moreover, it could also affect immune infiltration in the tumor microenvironment. Therefore, we aimed to explore the effects of lipid levels on tumor-infiltrating lymphocytes (TILs) and prognosis in patients with triple-negative breast cancer (TNBC). Methods: Samples from 222 patients with TNBC from July 2007 to December 2019 were obtained from the tissue specimen banks in 3 hospitals. The blood samples were used to detect the levels of lipid levels such as apolipoprotein B (Apo B), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C). The TILs in the 222 TNBC tissues were detected using hematoxylin and eosin (H&E) staining, and the relationship between the lipid levels, clinical characteristics, and prognosis was analyzed. Results: Among TNBC patients, the overall survival (OS) time and disease-free survival (DFS) time were lower in patients with high LDL-C levels than those with low LDL-C levels (p < 0.01, respectively). The DFS was shorter in patients with low stromal TIL (STIL) levels than those with moderate or high STIL levels (p = 0.023). Multifactor Cox regression analysis showed that LDL-C level, Apo B level, and lymphocyte-predominant breast cancer were independent risk factors for OS in TNBC patients. The number of positive lymph nodes, postoperative staging, and total amount of TILs were independent risk factors for DFS in TNBC patients. Conclusion: The LDL-C and STIL levels were correlated with survival and prognosis in patients with TNBC.

Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features.

PURPOSE: To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM). METHODS: We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n = 84) and a testing set (n = 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set. RESULTS: The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P < 0.05). CONCLUSION: The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.

Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study.

BACKGROUND: Immune checkpoint inhibitors (ICIs)-based therapy, is regarded as one of the major breakthroughs in cancer treatment. However, it is challenging to accurately identify patients who may benefit from ICIs. Current biomarkers for predicting the efficacy of ICIs require pathological slides, and their accuracy is limited. Here we aim to develop a radiomics model that could accurately predict response of ICIs for patients with advanced breast cancer (ABC). METHODS: Pretreatment contrast-enhanced CT (CECT) image and clinicopathological features of 240 patients with ABC who underwent ICIs-based treatment in three academic hospitals from February 2018 to January 2022 were assigned into a training cohort and an independent validation cohort. For radiomic features extraction, CECT images of patients 1 month prior to ICIs-based therapies were first delineated with regions of interest. Data dimension reduction, feature selection and radiomics model construction were carried out with multilayer perceptron. Combined the radiomics signatures with independent clinicopathological characteristics, the model was integrated by multivariable logistic regression analysis. RESULTS: Among the 240 patients, 171 from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center were evaluated as a training cohort, while other 69 from Sun Yat-sen University Cancer Center and the First Affiliated Hospital of Sun Yat-sen University were the validation cohort. The area under the curve (AUC) of radiomics model was 0.994 (95% CI: 0.988 to 1.000) in the training and 0.920 (95% CI: 0.824 to 1.000) in the validation set, respectively, which were significantly better than the performance of clinical model (0.672 for training and 0.634 for validation set). The integrated clinical-radiomics model showed increased but not statistical different predictive ability in both the training (AUC=0.997, 95% CI: 0.993 to 1.000) and validation set (AUC=0.961, 95% CI: 0.885 to 1.000) compared with the radiomics model. Furthermore, the radiomics model could divide patients under ICIs-therapies into high-risk and low-risk group with significantly different progression-free survival both in training (HR=2.705, 95% CI: 1.888 to 3.876, p<0.001) and validation set (HR=2.625, 95% CI: 1.506 to 4.574, p=0.001), respectively. Subgroup analyses showed that the radiomics model was not influenced by programmed death-ligand 1 status, tumor metastatic burden or molecular subtype. CONCLUSIONS: This radiomics model provided an innovative and accurate way that could stratify patients with ABC who may benefit more from ICIs-based therapies.

Dynamic Constitutive Relationship of Mg-Gd-Y-Zr-Ag Alloy during High Temperature Deformation Process.

The thermal deformation behavior of the Mg-Gd-Y-Zr-Ag alloy was studied by isothermal hot compression tests at high temperatures. The flow stress increased with increased strain rates and decreased temperatures, first increasing and finally remaining stable with increased strain. A hot processing map was built. Using the processing map and microstructural analysis, the temperature should remain at 673-773 K for this alloy to ensure the deformation quality. The primary softening mechanism is discontinuous dynamic recrystallization (DDRX). Rising temperatures and declining strain rates facilitated the emergence and growth of Dynamic recrystallization (DRX) grains. An original JC (O-JC) model and a modified JC (M-JC) model were established. The M-JC model indicated a better prediction than the O-JC model. Still, it was deficient in predicting flow stresses with insufficient coupling effects. Hence, based on the M-JC model, a newly modified JC (NM-JC) model, which further enhances the interaction between strain and strain rate as well as strain and temperature, is proposed. Its projected values can better align with the tested values.

What is the appropriate genetic testing criteria for breast cancer in the Chinese population?-Analysis of genetic and clinical features from a single cancer center database.

BACKGROUND: Genetic testing plays an important role in guiding screening, diagnosis, and precision treatment of breast cancer (BC). However, the appropriate genetic testing criteria remain controversial. The current study aims to facilitate the development of suitable strategies by analyzing the germline mutational profiles and clinicopathological features of large-scale Chinese BC patients. METHODS: BC patients who had undergone genetic testing at the Sun Yat-sen University Cancer Center (SYSUCC) from September 2014 to March 2022 were retrospectively reviewed. Different screening criteria were applied and compared in the population cohort. RESULTS: A total of 1035 BC patients were enrolled, 237 pathogenic or likely pathogenic variants (P/LPV) were identified in 235 patients, including 41 out of 203 (19.6%) patients tested only for BRCA1/2 genes, and 194 out of 832 (23.3%) received 21 genes panel testing. Among the 235 P/LPV carriers, 222 (94.5%) met the NCCN high-risk criteria, and 13 (5.5%) did not. While using Desai's criteria of testing, all females diagnosed with BC by 60 years and NCCN criteria for older patients, 234 (99.6%) met the high-risk standard, and only one did not. The 21 genes panel testing identified 4.9% of non-BRCA P/LPVs and a significantly high rate of variants of uncertain significance (VUSs) (33.9%). The most common non-BRCA P/LPVs were PALB2 (11, 1.3%), TP53 (10, 1.2%), PTEN (3, 0.4%), CHEK2 (3, 0.4%), ATM (3, 0.4%), BARD1 (3, 0.4%), and RAD51C (2, 0.2%). Compared with BRCA1/2 P/LPVs, non-BRCA P/LPVs showed a significantly low incidence of NCCN criteria listed family history, second primary cancer, and different molecular subtypes. CONCLUSIONS: Desai's criteria might be a more appropriate genetic testing strategy for Chinese BC patients. Panel testing could identify more non-BRCA P/LPVs than BRCA1/2 testing alone. Compared with BRCA1/2 P/LPVs, non-BRCA P/LPVs exhibited different personal and family histories of cancer and molecular subtype distributions. The optimal genetic testing strategy for BC still needs to be investigated with larger continuous population studies.

Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia.

BACKGROUND: This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA). METHODS AND RESULTS: This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05). CONCLUSION: pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.

18F-FDG-PET Radiomics Based on White Matter Predicts The Progression of Mild Cognitive Impairment to Alzheimer Disease: A Machine Learning Study.

RATIONALE AND OBJECTIVES: To build a model using white-matter radiomics features on positron-emission tomography (PET) and machine learning methods to predict progression from mild cognitive impairment (MCI) to Alzheimer disease (AD). MATERIALS AND METHODS: We analyzed the data of 341 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, of whom 102 progressed to AD during an 8-year follow-up. The patients were divided into the training (238 patients) and test groups (103 patients). PET-based radiomics features were extracted from the white matter in the training group, and dimensionally reduced to construct a psychoradiomics signature (PS), which was combined with multimodal data using machine learning methods to construct an integrated model. Model performance was evaluated using receiver operating characteristic curves in the test group. RESULTS: Clinical Dementia Rating (CDR) scores, Alzheimer's Disease Assessment Scale (ADAS) scores, and PS independently predicted MCI progression to AD on multivariate logistic regression. The areas under the curve (AUCs) of the CDR, ADAS and PS in the training and test groups were 0.683, 0.755, 0.747 and 0.737, 0.743, 0.719 respectively, and were combined using a support vector machine to construct an integrated model. The AUC of the integrated model in the training and test groups was 0.868 and 0.865, respectively (sensitivity, 0.873 and 0.839, respectively; specificity, 0.784 and 0.806, respectively). The AUCs of the integrated model significantly differed from those of other predictors in both groups (p < 0.05, Delong test). CONCLUSION: Our psych radiomics signature based on white-matter PET data predicted MCI progression to AD. The integrated model built using multimodal data and machine learning identified MCI patients at a high risk of progression to AD.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study.

Objective: To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH). Methods: A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models. Results: CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)]. Conclusion: pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.