Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network.

Background: Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. Methods: Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. Results: The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool's performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts' subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). Conclusions: The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.

Determinants of acute and subacute case-fatality in elderly patients with hypertensive intracerebral hemorrhage.

Background: Given that limited reports have described the survival and risk factors for elderly patients with hypertensive intracerebral hemorrhage (HICH), we aimed to develop a valid but simple prediction nomogram for the survival of HICH patients. Methods: All elderly patients ≥65 years old who were diagnosed with HICH between January 2011 and December 2019 were identified. We performed the least absolute shrinkage and selection operator (Lasso) on the Cox regression model with the R package glmnet. A concordance index was performed to calculate the nomogram discrimination; and calibration curves and decision curves were graphically evaluated by depicting the observed rates against the probabilities predicted by the nomogram. Results: A total of 204 eligible patients were analyzed, and over 20 % of the population was above the age of 80 (65-79 years old, n = 161; 80+ years old, n = 43). A hematoma volume ≥13.64 cm3 was associated with higher 7-day mortality (OR = 6.773, 95 % CI = 2.622-19.481; p < 0.001) and higher 90-day mortality (OR = 3.955, 95 % CI = 1.611-10.090, p = 0.003). A GCS score between 13 and 15 at admission was associated with a 7-day favorable outcome (OR = 0.025, 95 % CI = 0.005-0.086; p < 0.001) and a 90-day favorable outcome (OR = 0.033, 95 % CI = 0.010-0.099; p < 0.001). Conclusions: Our nomogram models were visualized and accurate. Neurosurgeons could use them to assess the prognostic factors and provide advice to patients and their relatives.

Constructing and Validating a Nomogram for Survival in Patients without Hypertension in Hypertensive Intracerebral Hemorrhage-Related Locations.

OBJECTIVE: We aimed to evaluate the risk factors for patients, who had hypertensive intracerebral hemorrhage (ICH)-specific location hemorrhage without hypertensive history, to elucidate a novel and detailed understanding. METHODS: We conducted a retrospective review to identify patients diagnosed with hemorrhage in hypertensive ICH-specific locations without hypertensive history between January 2011 and December 2019 from West China Hospital. A least absolute shrinkage and selector operation (LASSO) algorithm was used to select the optimal prognostic factors, and then we performed a multivariable logistic analysis. To verify the accuracy of the nomogram in predicting patient outcome, we used Harrell's statistics, area under the curve, and a calibration as well as decision curves. RESULTS: The LASSO method, at a tenfold cross-validation for 7-day mortality, 90-day mortality, and 90-day morbidity, was applied to construct the prognosis-predicting models. Both a higher Glasgow Coma Scale (GCS) score at admission and larger hematoma volume ≥13.64 mL were independently associated with better survival at 7 days and 90 days in multivariate analysis. Lactic dehydrogenase >250 IU/L and neutrophilic granulocyte/lymphocyte ratio in 1 increase were significantly associated with poor outcome at 90 days. Only one factor (GCS score at 7 days) influencing 90-day morbidity remained in a LASSO model. CONCLUSIONS: In this study, the GCS score, hematoma volume, and other laboratory factors (Lactic dehydrogenase and neutrophilic granulocyte/lymphocyte ratio) were related to survival. Our current findings of the specific location ICH need to be proven by a large randomized controlled trial study.

Promising novel biomarkers and therapy targets: The application of cell-free seminal nucleotides in male reproduction research.

Liquid biopsy has the advantage of diagnosing diseases in a non-invasive manner. Seminal plasma contains secretions from the bilateral testes, epididymides, seminal vesicles, bulbourethral glands, and the prostate. These organs are relatively small and contain delicate tubes that are prone to damage by invasive diagnosis. Cell-free seminal nucleic acids test is a newly emerged item in liquid biopsy. Here, we present a comprehensive overview of all known cell-free DNA and cell-free RNAs (mRNA, miRNA, lncRNA, circRNA, piRNA, YRNA, tsRNA, etc.) and discuss their roles as biomarker candidates in liquid biopsy. With great advantages, including high stability, sensitivity, representability, and non-invasiveness, cell-free DNA/RNAs may be developed as promising biomarkers for the screening, diagnosis, prognosis, and follow-up of diseases in semen-secreting organs. Moreover, RNAs in semen may participate in important processes, including sperm maturation, early embryo development, and transgenerational disease inheritance, which may be developed as potential treatment targets for future clinical use.

Predictive value of a serum tumor biomarkers scoring system for clinical stage II/III rectal cancer with neoadjuvant chemoradiotherapy.

BACKGROUND: Multiple classes of molecular biomarkers have been studied as potential predictors for rectal cancer (RC) response. Carcinoembryonic antigen (CEA) is the most widely used blood-based marker of RC and has proven to be an effective predictive marker. Cancer antigen 19-9 (CA19-9) is another tumor biomarker used for RC diagnosis and postoperative monitoring, as well as monitoring of the therapeutic effect. Using a panel of tumor markers for RC outcome prediction is a practical approach. AIM: To assess the predictive effect of pre-neoadjuvant chemoradiotherapy (NCRT) CEA and CA19-9 levels on the prognosis of stage II/III RC patients. METHODS: CEA and CA19-9 levels were evaluated 1 wk before NCRT. According to the receiver operating characteristic curve analysis, the optimal cut-off point of CEA and CA19-9 levels for the prognosis were 3.55 and 19.01, respectively. The novel serum tumor biomarker (NSTB) scores were as follows: score 0: Pre-NCRT CEA < 3.55 and CA19-9 < 19.01; score 2: Pre-NCRT CEA > 3.55 and CA19-9 > 19.01; score 1: Other situations. Pathological information was recorded according to histopathological reports after the operation. RESULTS: In the univariate analysis, pre-NCRT CEA < 3.55 [P = 0.025 for overall survival (OS), P = 0.019 for disease-free survival (DFS)], pre-NCRT CA19-9 < 19.01 (P = 0.014 for OS, P = 0.009 for DFS), a lower NSTB score (0-1 vs 2, P = 0.009 for OS, P = 0.005 for DFS) could predict a better prognosis. However, in the multivariate analysis, only a lower NSTB score (0-1 vs 2; for OS, HR = 0.485, 95%CI: 0.251-0.940, P = 0.032; for DFS, HR = 0.453, 95%CI: 0.234-0.877, P = 0.019) and higher pathological grade, node and metastasis stage (0-I vs II-III; for OS, HR = 0.363, 95%CI: 0.158-0.837, P = 0.017; for DFS, HR = 0.342, 95%CI: 0.149-0.786, P = 0.012) were independent predictive factors. CONCLUSION: The combination of post-NCRT CEA and CA19-9 was a predictive factor for clinical stage II/III RC patients receiving NCRT, and the combined index had a stronger predictive effect.

[Hematoma Segmentation of Spontaneous Intracerebral Hemorrhage Based on Watershed and Region-Growing Algorithm].

Objective: To establish a brain hematoma CT image segmentation method based on watershed and region-growing algorithm so as to measure hematoma volume quickly and accurately, to explore the consistency between the results of this segmentation method and those of manual segmentation, the clinical gold standard, and to compare the results of this method with the calculation of the two Tada formulas commonly used in clinical practice. Methods: The preoperative CT images of 152 patients who were treated for spontaneous cerebral hemorrhage at the Department of Neurosurgery, West China Hospital, Sichuan University between January 2018 and June 2019 were retrospectively collected. The CT images were randomly assigned, by using a random number table, to the training set, the test set and the validation set, which contained 100 patients, 22 patients and 30 patients, respectively. The labeling results of the training set and the test set were used in algorithm training and testing. Four methods, namely, manual segmentation, algorithm segmentation, i.e., segmentation calculation based on watershed and regional growth algorithm, Tada formula, i.e., the traditional Tada formula calculation, and accurate Tada formula, i.e., accurate Tada formula calculation based on 3D-Slicer, were applied on the validation set to measure the hematoma volume. The Digital Imaging and Communications in Medicine (DICOM) data of subjects meeting the selection criteria of the study were manually segmented by two experienced neurosurgeons. The hematoma segmentation model was built based on watershed algorithm and regional growth algorithm. Seed point selected by neurosurgeons was taken as the starting point of growth. Regional grayscale difference criterion combined with manual segmentation validation were adopted to determine the regional growth threshold that met the segmentation precision requirements for intracranial hematoma. Using manual segmentation as the gold standard, Bland-Altman consistency analysis was used to verify the consistency of the three other methods for measuring hematoma volume. Results: With manual segmentation as the gold standard, among the three methods of measuring hematoma volume, algorithm segmentation had the smallest percentage error, the narrowest range of difference, the highest intra-group correlation coefficient (0.987), good consistency, and the narrowest 95% limits of agreement ( LoA). The percentage error of its segmentation was not statistically significant for hematomas of different volumes. Conclusion: The segmentation method of spontaneous intracerebral hemorrhage based on watershed and regional growth algorithm shows stable measurement performance and good consistency with the clinical gold standard, which has considerable clinical significance, but it still needs further validation with more clinical samples.

[Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images].

OBJECTIVE: To examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images. METHODS: A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients' brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network. RESULTS: In the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network. CONCLUSION: The improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.

Construction of a nomogram to reveal the prognostic benefit of spontaneous intracranial hemorrhage among Chinese adults: a population-based study.

BACKGROUND AND PURPOSE: We aimed to build a nomogram, based on patients with spontaneous intracerebral hemorrhage (SICH), to predict the probability of mortality and morbidity at 7 days and 90 days, respectively. METHODS: We performed a retrospective study, with patients at less than 6 h from ictus admitted to the department of neurosurgery in a single institute, from January 2011 to December 2018. A total of 1036 patients with SICH were included, 486 patients (46.9%) were 47-66 years old at diagnosis, and 711 patients (68.6%) were male. The least absolute shrinkage and section operator method was performed to identify the key adverse factors predicting the outcomes in patients with SICH, and multivariate logistic regression analysis was built on these variables, and then the results were visualized by a nomogram. The discrimination of the prognostic models was measured and compared by means of Harrell's concordance index (C-index), calibration curve, area under the curve (AUC), and decision curve analysis (DCA). RESULTS: Multivariate logistic regression analysis revealed that factors affecting 7-day mortality, including the following: age, therapy, Glasgow Coma Scale (GCS) admission, location, ventricle involved, hematoma volume, white blood cell (WBC), uric acid (UA), and L-lactic dehydrogenase (LDH); and factors affecting 90-day mortality, including temperature, therapy, GCS admission, ventricle involved, WBC, international normalized ratio, UA, LDH, and systolic blood pressure. The C-index for the 7-day mortality and 90-day mortality prediction nomogram was 0.9239 (95% CI = 0.9061-0.9416) and 0.9241 (95% CI = 0.9064-0.9418), respectively. The AUC of 7-day mortality was 92.4, as is true of 90-day mortality. The calibration curve and DCA indicated that nomograms in our study had a good prediction ability. For 90-day morbidity, age, marital status, and GCS at 7-day remained statistically significant in multivariate analysis. The C-index for the prediction nomogram was 0.6898 (95% CI = 0.6511-0.7285), and the calibration curve, AUC as well as DCA curve indicated that the nomogram for the prediction of good outcome demonstrated good agreement in this cohort. CONCLUSIONS: Nomograms in this study revealed many novel prognostic demographic and laboratory factors, and the individualized quantitative risk estimation by this model would be more practical for treatment management and patient counseling.