Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma.

Objective: In individuals with stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy ± chemotherapy (PORT/CRT), the interaction between sarcopenia and malnutrition remains elusive, let alone employing a nomogram model based on radiomic features of psoas extracted at the level of the third lumbar vertebra (L3). This study was set to develop a radiomics-based nomogram model to predict malnutrition as per the Patient-Generated Subjective Global Assessment (PG-SGA) for individuals with CC. Methods: In total, 120 individuals with CC underwent computed tomography (CT) scans before PORT/CRT. The radiomic features of psoas at L3 were obtained from non-enhanced CT images. Identification of the optimal features and construction of the rad-score formula were conducted utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression to predict malnutrition in the training dataset (radiomic model). Identification of the major clinical factors in the clinical model was performed by means of binary logistic regression analysis. The radiomics-based nomogram was further developed by integrating radiomic signatures and clinical risk factors (combined model). The receiver operating characteristic (ROC) curves and decision curves analysis (DCA) were employed for the evaluation and comparison of the three models in terms of their predictive performance. Results: Twelve radiomic features in total were chosen, and the rad-score was determined with the help of the non-zero coefficient from LASSO regression. Multivariate analysis revealed that besides rad-score, age and Eastern Cooperative Oncology Group performance status could independently predict malnutrition. As per the data of this analysis, a nomogram prediction model was constructed. The area under the ROC curves (AUC) values of the radiomic and clinical models were 0.778 and 0.847 for the training and 0.776 and 0.776 for the validation sets, respectively. An increase in the AUC was observed up to 0.972 and 0.805 in the training and validation sets, respectively, in the combined model. DCA also confirmed the clinical benefit of the combined model. Conclusion: This radiomics-based nomogram model depicted potential for use as a marker for predicting malnutrition in stage IB1-IIA2 CC patients who underwent PORT/CRT and required further investigation with a large sample size.

A 5-year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms.

BACKGROUND: Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5-year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. METHODS: The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver-operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. RESULTS: A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors. CONCLUSIONS: The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision-making for nonmetastatic CC patients in the future.

White Matter Free Water is a Composite Marker of Cerebral Small Vessel Degeneration.

To investigate the association between white matter free water (FW) and common imaging markers of cerebral small vessel diseases (CSVD) in two groups of subjects with different clinical status. One hundred and forty-four community subjects (mean age 60.5) and 84 CSVD subjects (mean age 61.2) were retrospectively included in the present study. All subjects received multi-modal magnetic resonance imaging and clinical assessments. The association between white matter FW and common CSVD imaging markers, including white matter hyperintensities (WMH), dilated perivascular space (PVS), lacunes, and microbleeds, were assessed using simple and multiple regression analysis. The association between FW and cognitive scores were also investigated. White matter FW was positively associated with WMH volume (ß = 0.270, p = 0.001), PVS volume (ß = 0.290, p < 0.001), number of microbleeds (ß = 0.148, p = 0.043), and age (ß = 0.170, p = 0.036) in the community cohort. In the CSVD cohort, FW was positively associated with WMH volume (ß = 0.648, p < 0.001), PVS score (ß = 0.224, p < 0.001), number of lacunes (ß = 0.140, p = 0.046), and sex (ß = 0.125, p = 0.036). The associations between FW and cognitive scores were stronger than conventional CSVD markers in both datasets. White matter FW is a potential composite marker that can sensitively detect cerebral small vessel degeneration and also reflect cognitive impairments.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Patients With Newly Diagnosed Stage IVB Cervix Uteri Carcinoma.

Background: This study constructed and demonstrated a model to predict the overall survival (OS) of newly diagnosed distant metastatic cervical cancer (mCC) patients. Methods: The SEER (Surveillance, Epidemiology, and End Results) database was used to collect the eligible data, which from 2010 to 2016. Then these data were separated into training and validation cohorts (7:3) randomly. Cox regression analyses was used to identify parameters significantly correlated with OS. Harrell's Concordance index (C-index), calibration curves, and decision curve analysis (DCA) were further applied to verify the performance of this model. Results: A total of 2,091 eligible patients were enrolled and randomly split into training (n = 1,467) and validation (n = 624) cohorts. Multivariate analyses revealed that age, histology, T stage, tumor size, metastatic sites, local surgery, chemotherapy, and radiotherapy were independent prognostic parameters and were then used to build a nomogram for predicting 1 and 2-year OS. The C-index of training group and validation group was 0.714 and 0.707, respectively. The calibration curve demonstrated that the actual observation was in good agreement with the predicted results concluded by the nomogram model. Its clinical usefulness was further revealed by the DCAs. Based on the scores from the nomogram, a corresponding risk classification system was constructed. In the overall population, the median OS time was 23.0 months (95% confidence interval [CI], 20.5-25.5), 12.0 months (95% CI, 11.1-12.9), and 5.0 months (95% CI, 4.4-5.6), in the low-risk group, intermediate-risk group, and high-risk group, respectively. Conclusion: A novel nomogram and a risk classification system were established in this study, which purposed to predict the OS time with mCC patients. These tools could be applied to prognostic analysis and should be validated in future studies.

The Influence of Demographics and Vascular Risk Factors on Glymphatic Function Measured by Diffusion Along Perivascular Space.

Assessing glymphatic function using in-vivo imaging method is of great value for understanding its contribution to major brain diseases. In the present study, we aim to validate the association between a variety of risk factors and a potential index of glymphatic function-Diffusion Tensor Image Analysis Along the Perivascular Space (ALPS index). We enrolled 142 subjects from communities and performed multi-modality magnetic resonance imaging scans. The ALPS index was calculated from diffusion tensor imaging data, and its associations with demographic factors, vascular factors were investigated using regression analyses. We found that the ALPS index was negatively associated with age (ß = -0.284, p < 0.001). Compared to males, females had significantly higher ALPS index (ß = -0.243, p = 0.001). Hypertensive subjects had significantly lower ALPS index compared to non-hypertensive subjects (ß = -0.189, p = 0.013). Furthermore, venous disruption could decrease ALPS index (ß = -0.215, p = 0.003). In general, our results are in consistent with previous conceptions and results from animal studies about the pathophysiology of glymphatic dysfunction. Future studies utilizing this method should consider introducing the above-mentioned factors as important covariates.

Characterization of Lenticulostriate Arteries and Its Associations With Vascular Risk Factors in Community-Dwelling Elderly.

Lenticulostriate arteries (LSAs) supply blood to important subcortical areas and are, therefore, essential for maintaining the optimal functioning of the brain's most metabolically active nuclei. Past studies have demonstrated the potential for quantifying the morphology of LSAs as biomarkers of vascular fragility or underlying arteriopathies. Thus, the current study aims to evaluate the morphological features of LSAs, their potential value in cerebrovascular risk stratification, and their concordance with other vascular risk factors in community-dwelling elderly people. A total of 125 community-dwelling elderly subjects who underwent a brain MRI scan were selected from our prospectively collected imaging database. The morphological measures of LSAs were calculated on the vascular skeletons obtained by manual tracing, and the number of LSAs was counted. Additionally, imaging biomarkers of small vessel disease were evaluated, and the diameters of major cerebral arteries were measured. The effects of vascular risk factors on LSA morphometry, as well as the relationship between LSA measures and other imaging biomarkers, were investigated. We found that smokers had shorter (p = 0.04) and straighter LSAs (p < 0.01) compared to nonsmokers, and the presence of hypertension is associated with less tortuous LSAs (p = 0.03) in community-dwelling elderly. Moreover, the middle cerebral artery diameter was positively correlated with LSA count (r = 0.278, p = 0.025) and vessel tortuosity (r = 0.257, p = 0.04). The posterior cerebral artery diameter was positively correlated with vessel tortuosity and vessel length. Considering the scarcity of noninvasive methods for measuring small artery abnormalities in the brain, the LSA morphological measures may provide valuable information to better understand cerebral small vessel degeneration during aging.

Quantity and Morphology of Perivascular Spaces: Associations With Vascular Risk Factors and Cerebral Small Vessel Disease.

BACKGROUND: Perivascular spaces (PVSs) are important component of the brain glymphatic system. While visual rating has been widely used to assess PVS, computational measures may have higher sensitivity for capturing PVS characteristics under disease conditions. PURPOSE: To compute quantitative and morphological PVS features and to assess their associations with vascular risk factors and cerebral small vessel disease (CSVD). STUDY TYPE: Prospective. POPULATION: One hundred sixty-one middle-aged/later middle-aged subjects (age = 60.4 ± 7.3). SEQUENCE: 3D T1-weighted, T2-weighted and T2-FLAIR sequences, and susceptibility-weighted multiecho gradient-echo sequence on a 3 T scanner. ASSESSMENT: Automated PVS segmentation was performed on sub-millimeter T2-weighted images. Quantitative and morphological PVS features were calculated in white matter (WM) and basal ganglia (BG) regions, including volume, count, size, length (Lmaj ), width (Lmin ), and linearity. Visual PVS scores were also acquired for comparison. STATISTICAL TESTS: Simple and multiple linear regression analyses were used to explore the associations among variables. RESULTS: WM-PVS visual score and count were associated with hypertension (ß = 0.161, P < 0.05; ß = 0.193, P < 0.05), as were BG-PVS rating score, volume, count and Lmin (ß = 0.197, P < 0.05; ß = 0.170, P < 0.05; ß = 0.200, P < 0.05; ß = 0.172, P < 0.05). WM-PVS size was associated with diabetes (ß = 0.165, P < 0.05). WM-PVS and BG-PVS were associated with CSVD markers, especially white matter hyperintensities (WMHs) (P < 0.05). Multiple regression analysis showed that WM/BG-PVS quantitative measures were widely associated with vascular risk factors and CSVD markers (P < 0.05). Morphological measures were associated with WMH severity in WM region and also associated with lacunes and microbleeds (P < 0.05) in BG region. DATA CONCLUSION: These novel PVS measures may capture mild PVS alterations driven by different pathologies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Factors Associated With the Dilation of Perivascular Space in Healthy Elderly Subjects.

Background: The dilation of perivascular space (PVS) has been widely used to reflect brain degeneration in clinical brain imaging studies. However, PVS characteristics exhibit large differences in healthy subjects. Such variations need to be better addressed before PVS can be used to reflect pathological changes. In the present study, we aim to investigate the potential influence of several related factors on PVS dilation in healthy elderly subjects. Methods: One-hundred and three subjects (mean age = 59.5) were retrospectively included from a prospectively collected community cohort. Multi-modal high-resolution magnetic resonance imaging and cognitive assessments were performed on each subject. Machine-learning based segmentation methods were employed to quantify PVS volume and white matter hyperintensity (WMH) volume. Multiple regression analysis was performed to reveal the influence of demographic factors, vascular risk factors, intracranial volume (ICV), major brain artery diameters, and brain atrophy on PVS dilation. Results: Multiple regression analysis showed that age was positively associated with the basal ganglia (BG) (standardized beta = 0.227, p = 0.027) and deep white matter (standardized beta = 0.220, p = 0.029) PVS volume. Hypertension was positively associated with deep white matter PVS volume (standardized beta = 0.234, p = 0.017). Furthermore, we found that ICV was strongly associated with the deep white matter PVS volume (standardized beta = 0.354, p < 0.001) while the intracranial artery diameter was negatively associated with the deep white matter PVS volume (standardized beta = -0.213, p = 0.032). Conclusions: Intracranial volume has significant influence on deep white matter PVS volume. Future studies on PVS dilation should include ICV as an important covariate.

Deep white matter hyperintensity is associated with the dilation of perivascular space.

Understanding the pathophysiology of white matter hyperintensity (WMH) is necessary to reduce its harmfulness. Dilated perivascular space (PVS) had been found related to WMH. In the present study, we aimed to examine the topological connections between WMH and PVS, and to investigate whether increased interstitial fluid mediates the correlation between PVS and WMH volumes. One hundred and thirty-six healthy elder subjects were retrospectively included from a prospectively collected community cohort. Sub-millimeter T2 weighted and FLAIR images were acquired for assessing the association between PVS and WMH. Diffusion tensor imaging and free-water (FW) analytical methods were used to quantify white matter free water content, and to explore whether it mediates the PVS-WMH association. We found that most (89%) of the deep WMH lesions were spatially connected with PVS, exhibiting several interesting topological types. PVS and WMH volumes were also significantly correlated (r = 0.222, p < 0.001). FW mediated this association in the whole sample (ß = 0.069, p = 0.037) and in subjects with relatively high WMH load (ß = 0.118, p = 0.006). These findings suggest a tight association between PVS dilation and WMH formation, which might be linked by the impaired glymphatic drainage function and accumulated local interstitial fluid.

Disentangling the pathologies linking white matter hyperintensity and geriatric depressive symptoms in subjects with different degrees of vascular impairment.

BACKGROUND: White matter hyperintensity (WMH) is closely associated with geriatric depressive symptoms, but its underlying neural mechanism is unclear. We aim to disentangle the contribution of vascular degeneration and fiber disruption to depressive symptoms in elderly subjects at different clinical status. METHODS: One hundred and thirty-three normal elderly subjects, as well as 43 patients with cerebral small vessel disease (CSVD) were included. The Hamilton Depression Rating Scale (HAMD) was used to measure depressive symptoms. Based on the diffusion tensor imaging data, a free water elimination analytical model was adopted to reflect fiber tract disruption (measure: tissue fractional anisotropy, tFA) and increased white matter water content (measure: free water fraction, FW). RESULTS: We found that WMH severity was significantly correlated with decreased tFA and increased FW in all subjects. In normal elderly subjects, the HAMD score was correlated with mean tFA, but not FW. Compared to the traditional fractional anisotropy measure, tFA showed stronger correlation with clinical symptoms. In CSVD subjects, the correlation was only significant for FW, and marginally significant for tFA. LIMITATIONS: Most subjects had only mild to moderate depressive symptoms. Further validation in patients with major depressive disorder is needed to confirm these findings. CONCLUSIONS: The neural mechanisms of depressive symptoms may be different in elderly people with or without severe vascular damage. The free water elimination model may disentangle the effects of fiber disruption and increased free water, providing sensitive imaging markers that could potentially be used on monitoring disease treatment.

Enhancing bone regeneration by combining mesenchymal stem cell sheets with ß-TCP/COL-I scaffolds.

The combination of bone marrow-derived mesenchymal stem cells (BMSCs) and biological scaffolds has been demonstrated to be a promising strategy for bone regeneration. However, this method does not result in satisfactory bone regeneration, because the BMSCs are dispersed in the biological scaffolds. The current study developed a new bone regeneration system, which combines synthetic porous three-dimensional scaffolds of ß-TCP/COL-I composite with cultured osteogenic sheets of BMSCs. Activity of alkaline phosphatase (ALP), a marker of bone regeneration, was assayed in vitro using enzyme-linked immunosorbent assays and quantitative real-time polymerase chain reaction. In vivo bone regeneration was assayed in male nude mice. The study samples were BMSC sheet, scaffold/scattered BMSCs, scaffold/BMSC sheet, and scaffold alone. The samples were implanted dorsally in the mice. In vitro analysis showed that ß-TCP/COL-I scaffold combined with BMSC sheets significantly upregulated both gene expression and protein levels of ALP, osteocalcin, and osteopontin. Histological and micro-computed tomography showed that the only implants that demonstrated new bone formation after 4 weeks were scaffold/BMSC sheet implants. These results underscore the crucial requirement of a synergistic effect of ß-TCP/COL-I scaffolds and BMSC sheets. This could be a promising novel strategy for bone tissue engineering. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 2037-2045, 2018.

Pretreatment nutritional risk scores and performance status are prognostic factors in esophageal cancer patients treated with definitive chemoradiotherapy.

This study evaluated the prognostic effects of nutritional risk scores and performance status (PS) on unresectable locally advanced esophageal cancer (LAEC) patients who were treated with definitive concurrent chemoradiotherapy (dCRT). A total of 202 LAEC patients from four different cancer centers were retrospectively reviewed. Nutritional risk and PS were measured using the Nutritional Risk Screening 2002 (NRS-2002) scores and Eastern Cooperative Oncology Group (ECOG) scales. Outcomes were clinical response rate, overall survival (OS) and progression-free survival (PFS). Multivariate analysis of predictive factors of response to dCRT and survival were performed using a logistic regression and a Cox model, respectively. The majority of patients (71.8%) had an ECOG PS score of 0-1, and 52.5% (n=106) of patients were identified as having nutritional risk (NRS-2002 ≥3) upon treatment initiation. There was no correlation between NRS-2002 scores and ECOG PS (Spearman's ρ=0.046; P=0.516). In multivariate analysis, NRS-2002 scores (P=0.002, HR 2.805, 95%CI: 1.445-5.446) and ECOG PS (P=0.015, HR 2.719, 95%CI: 1.218-6.067) were independent prognostic factors for the response to dCRT. NRS-2002 scores (OS: HR 1.530, 95%CI 1.059-2.209; P=0.023; PFS: HR 1.517, 95%CI 1.105-2.082; P=0.010) and ECOG PS (OS: HR 1.729, 95%CI 1.185-2.522; P=0.005; PFS: HR 1.678, 95%CI 1.179-2.387; P=0.004) were both independent prognostic factors for OS and PFS. In conclusions, NRS-2002 scores and ECOG PS scales both have prognostic effects on clinical response and survival in LAEC, but a significant association of NRS-2002 scores and ECOG PS were not observed.

BMP-2 plasmid DNA-loaded chitosan films - A new strategy for bone engineering.

OBJECTIVES: Bone defects are common in every area of medicine and remain a clinical challenge. Tissue engineering has led to promising new strategies in accelerating bone repair. Bone morphogenetic proteins (BMPs) play crucial roles in bone regeneration, but are required in supra-physiological doses, which are expensive and produce severe side effects. METHODS: To address these issues, we prepared BMP-2 plasmid DNA-loaded chitosan films, and examined their effects on mouse osteoblast-like MC3T3-E1 cell morphology, proliferation, and runt-related transcription factor 2 (RUNX2) expression. In vivo testing was performed using calvarial critical-sized defects and histomorphometry in 36 Sprague-Dawley rats. Unloaded chitosan films and empty defects served as controls. RESULTS: In contrast to the controls, cells grown on BMP-2 plasmid DNA-loaded chitosan films had well established filopodia and lamellipodia, significantly higher proliferation 2, 4, and 6 days post-seeding (P ≤ 0.05), and higher nuclear RUNX2 expression. In vivo, new bone growth was significantly greater in the BMP-2 group than in the control groups at 4, 8, and 12 weeks (P ≤ 0.01). CONCLUSIONS: Based on our study findings, BMP-2 plasmid DNA-loaded chitosan films provide an effective strategy for GBR, combining cellular compatibility with biocapability in vivo.