Presentation and Management of Granulomatous Mastitis in the United States: Results of an American Society of Breast Surgeons Registry Study.

BACKGROUND: Granulomatous mastitis (GM) is a benign, chronic, inflammatory disease lacking clear treatment guidelines. The purpose of this American Society of Breast Surgeons (ASBrS) prospective, multisite registry was to characterize the presentation of GM and identify treatment strategies associated with symptom resolution and optimal cosmesis. METHODS: ASBrS members entered data into a registry on patient demographics, treatment, symptoms, and cosmesis over a 1-year period. Initial symptoms were graded as mild, moderate, or severe. The Chi-square test and logistic regression were used to identify factors related to symptom improvement and cosmesis. RESULTS: Overall, 112 patients with a mean age of 36 years were included. More patients were Hispanic (49.1%) and from the Southwest (41.1%), and management included observation (4.5%), medical (70.5%), surgical (5.4%), or combination treatment (19.6%). Immunosuppression was used in 83 patients (74.1%), including 43 patients who received intralesional steroid injections. Patients with severe symptoms were more likely to undergo surgical intervention compared with those with mild or moderate symptoms (21.4% vs. 0% and 7.5%, respectively; p = 0.004). Within 1 year, 85 patients (75.9%) experienced symptom improvement and/or resolution at a median of 3 months. Receipt of immunosuppressive therapy was predictive of improvement or resolution at 1 month (odds ratio 4.22; p = 0.045). One-year physician-assessed cosmesis was excellent or good for 20/35 patients (57.1%) and was not associated with type of treatment or symptom severity. CONCLUSION: Although GM can have a protracted course, the majority of patients in this registry resolved within 1 year, with good cosmetic result. Treatment with immunosuppression appears to be most beneficial, and a symptom-based algorithm may be helpful to guide treatment.

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. PURPOSE: The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. METHODS: Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. RESULTS: Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. CONCLUSIONS: 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.

Let's TOC Fertility: A stepped wedge cluster randomized controlled trial of the Telehealth Oncofertility Care (TOC) intervention in children, adolescent and young adult cancer survivors.

INTRODUCTION: Children, adolescent, and young adult cancer survivors experience overall increased risks of infertility that are preventable through effective fertility preservation services prior to starting cancer treatment. Oncofertility care is the evidence-based practice of informing newly diagnosed cancer patients about their reproductive risks and supporting shared decision-making on fertility preservation services. Despite longstanding clinical guidelines, oncofertility care delivery continues to be limited and highly variable across adult and pediatric oncology settings. MATERIALS AND METHODS: We describe the design of a stepped wedge cluster randomized clinical trial to evaluate the effectiveness of the multi-component Telehealth Oncofertility Care (TOC) intervention conducted in 20 adult and pediatric oncology clinics across three health systems in Southern California. Intervention components are: 1) electronic health record-based oncofertility needs screen and referral pathway to a virtual oncofertility hub; 2) telehealth oncofertility counseling through the hub; and 3) telehealth oncofertility financial navigation through the hub. We hypothesize the intervention condition will be associated with increased proportions of patients who engage in goal-concordant oncofertility care (i.e., engagement in reproductive risk counseling and fertility preservation services that meet the patient's fertility goals) and improved patient-reported outcomes, compared to the usual care control condition. We will also evaluate intervention implementation in a mixed-methods study guided by implementation science frameworks. DISCUSSION: Our overall goal is to speed implementation of a scalable oncofertility care intervention at cancer diagnosis for children, adolescent and young adult cancer patients to improve their future fertility and quality of life. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT05443737.

The effect of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids.

The use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance is a promising precision medicine approach, and its potential to inform clinical decisions is now being tested in several large multiinstitutional clinical trials. PDOs are cultivated in the extracellular matrix from basement membrane extracts (BMEs) that are most commonly acquired commercially. Each clinical site utilizes distinct BME lots and may be restricted due to the availability of commercial BME sources. However, the effect of different sources of BMEs on organoid drug response is unknown. Here, we tested the effect of BME source on proliferation, drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel compared with Cultrex and UltiMatrix. However, we observed no substantial effect on drug response when organoids were cultured in Matrigel, Cultrex, or UltiMatrix. We also did not observe major shifts in gene expression across the different BME sources, and PDOs maintained their classical or basal-like designation. Overall, we found that the BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves or drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.

The impact of extracellular matrix on the precision medicine utility of pancreatic cancer patient-derived organoids.

The use of patient-derived organoids (PDOs) to characterize therapeutic sensitivity and resistance (pharmacotyping) is a promising precision medicine approach. The potential of this approach to inform clinical decisions is now being tested in several large multi-institutional clinical trials. PDOs are cultivated in extracellular matrix from basement membrane extracts (BMEs) that are most commonly acquired commercially. Each clinical site utilizes distinct BME lots and may be restricted due to the availability of commercial BME sources. However, the impact of different sources and lots of BMEs on organoid drug response is unknown. Here, we tested the impact of BME source and lot on proliferation, chemotherapy and targeted therapy drug response, and gene expression in mouse and human pancreatic ductal adenocarcinoma (PDA) organoids. Both human and mouse organoids displayed increased proliferation in Matrigel (Corning) compared to Cultrex (RnD) and UltiMatrix (RnD). However, we observed no substantial impact on drug response when oragnoids were cultured in Matrigel, Cultrex, or UltiMatrix. We also did not observe major shifts in gene expression across the different BME sources, and PDOs maintained their Classical or Basal-like designation. Overall, we find that BME source (Matrigel, Cultrex, UltiMatrix) does not shift PDO dose-response curves and drug testing results, indicating that PDO pharmacotyping is a robust approach for precision medicine.

Outcomes from a 3-fraction high-dose-rate brachytherapy regimen for patients with cervical cancer.

PURPOSE: To estimate local control, survival, and toxicity associated with a 3-fraction (3F) image-guided brachytherapy (IGBT) regimen compared to longer fraction (LF) for cervical cancer. METHODS: 150 patients treated between 2015-2020 with 3F (24Gy in 3 fractions) or LF (28...30 Gy in 4-5 fractions) were reviewed. The primary outcome was 2-year local failure. We compared overall survival (OS), disease-free survival (DFS), hospitalizations, and toxicity. RESULTS: There were 32 patients in the 3F group and 118 in the LF group, with a median follow up of 22 months. The 3F had worse performance status (p = 0.01) but otherwise similar characteristics. The 2-year local failure rate was 3.6% (95% CI 0%, 10.6%) for 3F, and 7.5% (95% CI 2.4%, 12.6%) for LF. The univariable hazard ratio (HR) for local failure for 3F was 0.43 (0.05, 3.43; p = 0.43). Moreover, 2 of 32 (6.3%) 3F patients experienced Grade ...3 toxicity compared to 7 of 118 (5.9%) LF patients (p = 1.0), with no difference in hospitalization within 2 years (p = 0.66) and no treatment-related deaths. CONCLUSIONS: Local control was excellent, with long term survival and toxicity similar between the groups. These findings support consideration of 3F.

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion. Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P < 0.001), impaired cognitive function (P < 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient -0.017, P < 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.

Quantitative prediction of aspiration risk in head and neck cancer patients treated with radiation therapy.

OBJECTIVE: To determine characteristics most strongly associated with risk for aspiration events among head and neck cancer (HNC) patients undergoing curative intent treatment. MATERIALS AND METHODS: This was a retrospective, cross-sectional study of 106 patients with previously untreated HNC who received definitive or postoperative radiation therapy (RT) +/- systemic therapy with curative intent. Patients who received post-treatment videofluoroscopic swallow study (VFSS) between 2018-2021 were included. Using ordinal multivariable logistic regression, we modeled the effects of age (>60 years vs. ≤60 years), sex, body mass index (BMI) (>20 kg/m2 vs. ≤20 kg/m2), American Joint Committee on Cancer 8th edition stage (I-II vs. III-IVB), treatment with cisplatin (vs. other or no systemic therapy), post-operative status, primary site (oral cavity vs. P16+ oropharynx vs. P16- Mucosal Site vs. other), and quantitative VFSS measures on Penetration-Aspiration Scale (PAS) score. RESULTS AND CONCLUSION: On ordinal multivariable logistic regression, age >60 years (odds ratio (OR): 3.91, 95% confidence interval (CI): 1.29, 11.9), advanced stage (stage III-IVB) (OR: 3.13, 95% CI: 1.23, 7.79), pharyngeal constriction ratio (PCR) >0.25 (OR: 3.65, 95% CI: 1.14, 11.7), and bolus clearance ratio (BCR) > 0.10 (OR: 3.42, 95% CI: 1.20, 9.75) were found to be significant risk factors for higher PAS scores. Patients with ≥ 2 pre-treatment risk factors had statistically significant increased risk for post-treatment aspiration (OR 2.52, 95% CI: 1.31, 4.86) on ordinal logistic regression. This model could be useful to direct high-risk patients toward interventions designed to reduce risk of aspiration events.

A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.

Detection of Carbohydrate Antigen 50 Based on a Novel Miniaturized Chemiluminescence Analyzer Enables Large-Scale Cancer Early Screening in Grassroots Community.

Early screening of cancer can effectively prolong survival time and reduce cancer mortality. However, the existing health-monitoring devices can only be carried out in professional laboratories, so large-scale early cancer screening in resource-limited settings is hardly achieved. To embrace the challenge, we developed a novel chemiluminescence immunoassay (CLIA) analyzer that does not require a professional operation. Then, it was applied to detect carbohydrate antigen 50 (CA50), a non-organ-specific tumor marker for screening various cancers. As a result, the analyzer exhibited excellent performance that the total assay time was only 15 min, and the detection limit reached 0.057 U ml-1. A coefficient of variance (CV) less than 15% was well-controlled for both intra- and inter-assay precision, and the linear range was 0-500 U ml-1. More importantly, this analyzer can continuously detect 60 samples per hour without any professional paramedic. Finally, this analyzer has been applied to evaluate clinical samples and the detected results showed a good correlation with the clinical test results (correlation coefficient, 0.9958). These characteristics exactly meet large-scale and high-throughput early screening of cancer. Thus, this miniaturized analyzer for CA50 detection is promising to achieve early large-scale screening of cancer in the resource-limited grassroots community.

Validation of NRG Oncology's prognostic nomograms for oropharyngeal cancer in the Veterans Affairs database.

BACKGROUND: To test whether nomograms developed by NRG Oncology for oropharyngeal squamous cell carcinoma (OPSCC) patients could be validated in an independent population-based sample. METHODS: The authors tested nomograms for estimating progression-free survival (PFS) and overall survival (OS) in patients from the Veterans Health Administration with previously untreated locoregionally advanced OPSCC, diagnosed between 2008 and 2017, managed with definitive radiotherapy with or without adjuvant systemic therapy. Covariates were age, performance status, p16 status, T/N category, smoking history, education history, weight loss, marital status, and anemia. We used multiple imputation to handle missing data and performed sensitivity analyses on complete cases. Validation was assessed via Cox proportional hazards models, log-rank tests, and c-indexes. RESULTS: A total of 4007 patients met inclusion criteria (658 patients had complete data). Median follow-up time was 3.20 years, with 967 progression events and 471 noncancer deaths. Each risk score was associated with poorer outcomes per unit increase (PFS score, hazard ratio [HR], 1.42 [1.37-1.47]; OS score, HR, 1.40 [1.34-1.45]). By risk score quartile, 2-year PFS estimates were 89.2%, 78.5%, 65.8%, and 48.3%; OS estimates were 92.6%, 83.6%, 73.9%, and 51.3%, respectively (P < .01 for all comparisons). C-indices for models of PFS and OS were 0.65 and 0.67, for all patients, respectively (0.69 and 0.73 for complete cases). The nomograms slightly overestimated PFS and OS in the overall cohort but exhibited high agreement in complete cases. CONCLUSIONS: NRG nomograms were effective for predicting PFS and OS for patients with OPSCC, supporting their broader applicability in the OPSCC population undergoing definitive radiotherapy.

Incidence of extrauterine growth retardation and its risk factors in very preterm infants during hospitalization: a multicenter prospective study. / ä½é™¢æžæ—©äº§å„¿å®«å¤–ç”Ÿé•¿è¿Ÿç¼“åŠç›¸å ³å› ç´ çš„å¤šä¸­å¿ƒå‰çž»æ€§ç ”ç©¶.

OBJECTIVES: To investigate the incidence of extrauterine growth retardation (EUGR) and its risk factors in very preterm infants (VPIs) during hospitalization in China. METHODS: A prospective multicenter study was performed on the medical data of 2 514 VPIs who were hospitalized in the department of neonatology in 28 hospitals from 7 areas of China between September 2019 and December 2020. According to the presence or absence of EUGR based on the evaluation of body weight at the corrected gestational age of 36 weeks or at discharge, the VPIs were classified to two groups: EUGR group (n=1 189) and non-EUGR (n=1 325). The clinical features were compared between the two groups, and the incidence of EUGR and risk factors for EUGR were examined. RESULTS: The incidence of EUGR was 47.30% (1 189/2 514) evaluated by weight. The multivariate logistic regression analysis showed that higher weight growth velocity after regaining birth weight and higher cumulative calorie intake during the first week of hospitalization were protective factors against EUGR (P<0.05), while small-for-gestational-age birth, prolonged time to the initiation of total enteral feeding, prolonged cumulative fasting time, lower breast milk intake before starting human milk fortifiers, prolonged time to the initiation of full fortified feeding, and moderate-to-severe bronchopulmonary dysplasia were risk factors for EUGR (P<0.05). CONCLUSIONS: It is crucial to reduce the incidence of EUGR by achieving total enteral feeding as early as possible, strengthening breastfeeding, increasing calorie intake in the first week after birth, improving the velocity of weight gain, and preventing moderate-severe bronchopulmonary dysplasia in VPIs.

Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data.

PURPOSE: Cancer treatments can paradoxically appear to reduce the risk of noncancer mortality in observational studies, due to residual confounding. Here we introduce a method, Bias Reduction through Analysis of Competing Events (BRACE), to reduce bias in the presence of residual confounding. EXPERIMENTAL DESIGN: BRACE is a novel method for adjusting for bias from residual confounding in proportional hazards models. Using standard simulation methods, we compared BRACE with Cox proportional hazards regression in the presence of an unmeasured confounder. We examined estimator distributions, bias, mean squared error (MSE), and coverage probability. We then estimated treatment effects of high versus low intensity treatments in 36,630 prostate cancer, 4,069 lung cancer, and 7,117 head/neck cancer patients, using the Veterans Affairs database. We analyzed treatment effects on cancer-specific mortality (CSM), noncancer mortality (NCM), and overall survival (OS), using conventional multivariable Cox and propensity score (adjusted using inverse probability weighting) models, versus BRACE-adjusted estimates. RESULTS: In simulations with residual confounding, BRACE uniformly reduced both bias and MSE. In the absence of bias, BRACE introduced bias toward the null, albeit with lower MSE. BRACE markedly improved coverage probability, but with a tendency toward overcorrection for effective but nontoxic treatments. For each clinical cohort, more intensive treatments were associated with significantly reduced hazards for CSM, NCM, and OS. BRACE attenuated OS estimates, yielding results more consistent with findings from randomized trials and meta-analyses. CONCLUSIONS: BRACE reduces bias and MSE when residual confounding is present and represents a novel approach to improve treatment effect estimation in nonrandomized studies.

Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET/CT Radiomics.

Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting nontumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating nontumor radiomics, including whole-body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods: We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole-body 18F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT image. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT scan, and a semiautomated approach combining seed-growing and manual contour review generated whole-body muscle, bone, and fat segmentations on each PET/CT image. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. Ninety-five patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including 1 clinical biomarker (whether or not a tumor was stage III-IVA), 2 GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), 1 PTV radiomic biomarker (major axis length), and 1 whole-body radiomic biomarker (CT bone root mean square). In particular, stratification into high- and low-risk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio of 0.019 (95% CI, 0.004, 0.082), an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 (95% CI, 0.16, 0.83). Conclusion: Incorporating nontumor radiomic biomarkers can improve the performance of prognostic models compared with using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.

A point-of-care chemiluminescence immunoassay for pepsinogen I enables large-scale community health screening.

Pepsinogen I (PGI) can reflect the morphology and function of the gastric mucosa. Accordingly, the large-scale community health screening of PGI can dramatically increase the early diagnosis rate of gastric cancer. However, PGI testing can only be carried out in comprehensive hospitals and health examination centers. To ameliorate this issue, a point-of-care chemiluminescent immunoassay for PGI was developed in a fully automated miniaturized instrument. This instrument was especially developed for health check-ups in the grassroots communities; its volume of which is only 0.18 m3. Critically, the entire detection process for a single sample only requires 20 min, and the samples can be loaded continuously, making the method suitable for high-throughput analysis. The assay displayed an excellent detection limit of 0.048 ng/mL with a broad detection range of 0-200 ng/mL. Furthermore, this assay exhibited high sensitivity and specificity, had low intra- and inter-assay coefficients of variation (<10%), and was not affected after storage at 37 °C for 7 days. The assay was used to detect PGI in 95 clinical serum samples, and the results were highly correlated with those that were clinically tested (correlation coefficient, R2 = 0.998). Hence, the method established in this work has great application value and can be broadly applied for the large-scale screening of gastric cancer in resource-limited areas.

Deubiquitylation and stabilization of ARMC5 by ubiquitin-specific processing protease 7 (USP7) are critical for RCC proliferation.

The ubiquitin-proteasome system is an essential regulator of ARMC5, which serves as a new tumour suppressor protein for inhibiting meningiomas and hereditary adrenocortical tumorigenesis. However, the precise mechanism for the deubiquitination of ARMC5 is still not fully understood. A Western blot analysis of ARMC5 was performed and showed that the expression of ARMC5 was decreased in the renal cancer cell tissues and lines. By screening a deubiquitinase library, we identified USP7 as a potential ARMC5 associated deubiquitinase. In this paper, we demonstrated that there was an interaction between USP7 and ARMC5 in vivo and in vitro. Employing the overexpression and knockdown assay indicated that USP7 could greatly increase the steady state of ARMC5 through the ubiquitin-proteasome pathway and regulate ARMC5 ubiquitination. Moreover, USP7 altered cell cycle G1/S phases and regulated renal cancer cell proliferation by targeting ARMC5. Together, these results suggest that USP7 plays an important role in the RCC proliferation through modulating ARMC5 stability.

Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival.

BACKGROUND AND PURPOSE: Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS: A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS: Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS: Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment.

Intratumoral Heterogeneity of Glioblastoma Infiltration Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging.

BACKGROUND: Glioblastoma is a heterogeneous disease characterized by its infiltrative growth, rendering complete resection impossible. Diffusion tensor imaging (DTI) shows potential in detecting tumor infiltration by reflecting microstructure disruption. OBJECTIVE: To explore the heterogeneity of glioblastoma infiltration using joint histogram analysis of DTI, to investigate the incremental prognostic value of infiltrative patterns over clinical factors, and to identify specific subregions for targeted therapy. METHODS: A total of 115 primary glioblastoma patients were prospectively recruited for surgery and preoperative magnetic resonance imaging. The joint histograms of decomposed anisotropic and isotropic components of DTI were constructed in both contrast-enhancing and nonenhancing tumor regions. Patient survival was analyzed with joint histogram features and relevant clinical factors. The incremental prognostic values of histogram features were assessed using receiver operating characteristic curve analysis. The correlation between the proportion of diffusion patterns and tumor progression rate was tested using Pearson correlation. RESULTS: We found that joint histogram features were associated with patient survival and improved survival model performance. Specifically, the proportion of nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion was correlated with tumor progression rate (P = .010, r = 0.35), affected progression-free survival (hazard ratio = 1.08, P < .001), and overall survival (hazard ratio = 1.36, P < .001) in multivariate models. CONCLUSION: Joint histogram features of DTI showed incremental prognostic values over clinical factors for glioblastoma patients. The nonenhancing tumor subregion with decreased isotropic diffusion and increased anisotropic diffusion may indicate a more infiltrative habitat and potential treatment target.