IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction. 相似文献
Objective The present study aims to investigate the concentrations of Hg and its aspects methyl mercury(Me-Hg) and inorganic mercury(I-Hg) in the biological samples(BSs) of fluorescent lamp industries workers(FLIWs).Methodology Different BSs including red blood cells(RBCs),plasma,urine,hair and nails were collected from the workers exposed to Hg and unexposed persons were selected as control group to measure both the T-Hg concentration as well as its species in different biological samples through quantitative analysis.Health data was collected through questionnaire survey.Results The mean concentrations of T-Hg(31.9 μg/L),Me-Hg(27.7 μg/L),and I-Hg(5.36 μg/L) in RBCs were found significantly(P 0.001) higher among the workers(n = 40) as compared to the control group(n = 40).Similarly the mean Hg concentrations in plasma,urine,hair and nails were also significantly higher among the workers than the control group.The statistical relation between Hg concentration and demographic characteristics observed that workers experience and fish consumption has increased the Hg concentration while age,weight and smoking found no significant effect on Hg concentration in the BSs.Conclusion The study observed that the workers were highly exposed to high concentration of Hg and they are at a high health risk. 相似文献
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
IntroductionThe aims of this study were to analyze the pathological response, and survival outcomes of adenocarcinoma/adenosquamous (AC/ASC) versus squamous cell carcinoma (SCC) in patients with locally advanced cervical cancer (LACC) managed by chemoradiotherapy followed by radical surgery.MethodsRetrospective, multicenter, observational study, including patients with SCC and AC/ACS LACC patients treated with preoperative CT/RT followed by tailored radical surgery (RS) between 06/2002 and 05/2017. Clinical-pathological characteristics were compared between patients with SCC versus AC/ASC. A 1:3 ratio propensity score (PS) matching was applied to remove the variables imbalance between the two groups.ResultsAfter PS, 320 patients were included, of which 240 (75.0%) in the SCC group, and 80 (25.0%) in the AC/ASC group. Clinico-pathological and surgical baseline characteristics were balanced between the two study groups. Percentage of pathologic complete response was 47.5% in SCC patients versus 22.4% of AC/ASC ones (p < 0.001). With a median follow-up of 51 months (range:1–199), there were 54/240 (22.5%) recurrences in SCC versus 28/80 (35.0%) in AC/ASC patients (p = 0.027). AC/ASC patients experienced worse disease free (DFS), and overall survival (OS) compared to SCC patients (p = 0.019, and p = 0.048, respectively). In multivariate analysis, AC/ACS histotype, and FIGO stage were associated with worse DFS and OS.ConclusionIn LACC patients treated with CT/RT followed by RS, AC/ASC histology was associated with lower pathological complete response to CT/RT, and higher risk of recurrence and death compared with SCC patients. This highlights the need for specific therapeutic strategies based on molecular characterization to identify targets and develop novel treatments. 相似文献
Background: Most theoretical models of self-determination suggest that both environmental and personal factors influence the development of self-determination. The design and implementation of interventions must be conducted with foreknowledge of such mediating and moderating factors if the intervention is to be successful.
Methods: The purpose of this study was to examine the degree to which several personal factors and school characteristics affect and explain students’ self-determination. A total of 232 students with intellectual disability from Spain participated. Their self-determination level was assessed by the ARC-INICO Scale.
Results: Students with moderate levels of intellectual disability obtained significantly lower scores on self-determination than their peers with mild intellectual disability. There were significant differences in relation to the level of support needs and their experience with transition programs. The level of support needs was a significant predictor.
Conclusion: These findings contribute to current research in this field and practical implications were discussed. 相似文献