Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

AbstractA standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring system based on mucosal visibility was trained for deep learning in the training set. Performance of the trained software was evaluated in the validation set. Average cleansing score (1.0 to 5.0) by deep learning was compared to clinical grading (A to C) reviewed by clinicians. Cleansing scores decreased as clinical grading worsened (scores of 4.1, 3.5, and 2.9 for grades A, B, and C, respectively, P < 0.001). Adequate preparation was achieved for 91.7% of validation cases. The average cleansing score was significantly different between adequate and inadequate group (4.0 vs. 2.9, P < 0.001). ROC curve analysis revealed that a cut-off value of cleansing score at 3.25 had an AUC of 0.977. Diagnostic yields for small, hard-to-find lesions were associated with high cleansing scores (4.3 vs. 3.8, P < 0.001). We developed a novel scoring software which calculates objective, automated cleansing scores for SB preparation. The cut-off value we suggested provides a standard criterion for adequate bowel preparation as a quality indicator.

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Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

British Journal of Radiology ◽

10.1259/bjr.20210191 ◽

2021 ◽

pp. 20210191

Author(s):

Liuhui Zhang ◽

Donggen Jiang ◽

Chujie Chen ◽

Xiangwei Yang ◽

Hanqi Lei ◽

...

Keyword(s):

Prostate Cancer ◽

Multiparametric Mri ◽

Curve Analysis ◽

Training Set ◽

Threshold Probability ◽

Roc Curve Analysis ◽

Aggressive Prostate Cancer ◽

Therapeutic Decisions ◽

Validation Set ◽

Radiomics Signature

Objectives: To develop and validate a noninvasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy. Methods: In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T2WI (n = 788) and DWI (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic (ROC) curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature. Results: nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%. Conclusions: The multiparametric MRI-based radiomics signature can potentially serve as a noninvasive tool for distinguishing between indolent and aggressive PCa prior to therapy. Advances in knowledge: The multiparametric MRI-based radiomics signature has the potential to noninvasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

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Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

Journal of Translational Medicine ◽

10.1186/s12967-020-02522-6 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Wei Ma ◽

Fangkun Zhao ◽

Xinmiao Yu ◽

Shu Guan ◽

Huandan Suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. Methods We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. Results A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e − 06 in the training set; p = 0.0069 in the validation set; p = 1.233e − 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204–1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004–1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128–1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

10.21203/rs.3.rs-31519/v1 ◽

2020 ◽

Author(s):

wei ma ◽

fangkun zhao ◽

xinmiao yu ◽

shu guan ◽

huandan suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancers development and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separatedinto training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate and multivariate Cox regression analyses. Results: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group( p= 1.215e−06 in the training set; p =0.0069 in the validation set; p =1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p <0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p <0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

10.21203/rs.3.rs-31519/v2 ◽

2020 ◽

Author(s):

wei ma ◽

fangkun zhao ◽

xinmiao yu ◽

shu guan ◽

huandan suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer.Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses.Results:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group(p=1.215e−06 in the training set; p=0.0069 in the validation set; p=1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set,0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p<0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p<0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways.Conclusions:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

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Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading

Digestive Endoscopy ◽

10.1111/den.13517 ◽

2019 ◽

Vol 32 (4) ◽

pp. 585-591 ◽

Cited By ~ 13

Author(s):

Tomonori Aoki ◽

Atsuo Yamada ◽

Kazuharu Aoyama ◽

Hiroaki Saito ◽

Gota Fujisawa ◽

...

Keyword(s):

Deep Learning ◽

Small Bowel ◽

Capsule Endoscopy ◽

Clinical Usefulness

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Prediction of vaginal birth after cesarean delivery in southeast China: A retrospective cohort study

10.21203/rs.3.rs-30089/v1 ◽

2020 ◽

Author(s):

Hua-Le Zhang ◽

Liang-Hui Zheng ◽

Li-Chun Cheng ◽

Zhao-Dong Liu ◽

Lu Yu ◽

...

Keyword(s):

Cesarean Section ◽

Pregnant Women ◽

Success Rate ◽

Gestational Age ◽

Curve Analysis ◽

Inclusion Criteria ◽

Vaginal Birth After Cesarean ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set

Abstract Objective To develop and validate a nomogram to better predict the vaginal birth after cesarean (VBAC) on the premise of clinical guide application. Methods We retrospectively identified hospitalised pregnant women who trial of labor after cesarean (TOLAC) between October 2015 and October 2017 using data from the Fujian Provincial Maternity and Children's Hospital. The inclusion criteria were as follows: Singleton pregnant women whose gestational age was above 37 weeks and underwent a primary cesarean section. Sociodemographic data and Clinical Characteristics were extracted. The samples were randomly divided into a training set and a validation set. Least absolute shrinkage and selection operator (LASSO) regression were used to select variables and construct of VBAC success rate in training set. The validation of the nomogram was performed using the concordance index (C-index), decision curve analysis (DCA), and calibration curves in the validation set. For comparison with published VBAC prediction models, the Grobman’s model was used. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. In multivariate logistic regression models, Maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The predictive model was constructed showed better discrimination in the validation series than Grobman’s model (c-index 0.906 VS 0.694, respectively). Decision curve analysis revealed that the new model resulted in a better clinical net benefit than the Grobman’s model. Conclusions The promotion of VBAC is helpful to reduce the cesarean section rate in China. On the basis of following the clinical practice guidelines, the TOLAC prediction model helps to improve the success rate of VBAC and has a potential contribution to the reduction of secondary cesarean section.

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Establishment and Validation of a Clinically Predictive Nomogram Model for Thyroid Carcinoma Patients

10.21203/rs.3.rs-123528/v1 ◽

2020 ◽

Author(s):

Ruyi Zhang ◽

Mei Xu ◽

Xiangxiang Liu ◽

Miao Wang ◽

Qiang Jia ◽

...

Keyword(s):

Thyroid Carcinoma ◽

External Validation ◽

Cancer Staging ◽

Curve Analysis ◽

Calibration Plot ◽

Net Benefit ◽

Training Set ◽

Decision Curve Analysis ◽

Validation Set ◽

Good Calibration

Abstract Objectives To develop a clinically predictive nomogram model which can maximize patients’ net benefit in terms of predicting the prognosis of patients with thyroid carcinoma based on the 8th edition of the AJCC Cancer Staging method. MethodsWe selected 134,962 thyroid carcinoma patients diagnosed between 2004 and 2015 from SEER database with details of the 8th edition of the AJCC Cancer Staging Manual and separated those patients into two datasets randomly. The first dataset, training set, was used to build the nomogram model accounting for 80% (94,474 cases) and the second dataset, validation set, was used for external validation accounting for 20% (40,488 cases). Then we evaluated its clinical availability by analyzing DCA (Decision Curve Analysis) performance and evaluated its accuracy by calculating AUC, C-index as well as calibration plot.ResultsDecision curve analysis showed the final prediction model could maximize patients’ net benefit. In training set and validation set, Harrell’s Concordance Indexes were 0.9450 and 0.9421 respectively. Both sensitivity and specificity of three predicted time points (12 Months,36 Months and 60 Months) of two datasets were all above 0.80 except sensitivity of 60-month time point of validation set was 0.7662. AUCs of three predicted timepoints were 0.9562, 0.9273 and 0.9009 respectively for training set. Similarly, those numbers were 0.9645, 0.9329, and 0.8894 respectively for validation set. Calibration plot also showed that the nomogram model had a good calibration.ConclusionThe final nomogram model provided with both excellent accuracy and clinical availability and should be able to predict patients’ survival probability visually and accurately.

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MRI-Based Radiomics Nomogram To Predict Intraoperative Hemorrhage of Placenta Previa

10.21203/rs.3.rs-457028/v2 ◽

2021 ◽

Author(s):

Ye Song ◽

Liping Zhu ◽

Dali Chen ◽

Yongmei Li ◽

Qi Xi ◽

...

Keyword(s):

Predictive Accuracy ◽

Placenta Previa ◽

Preoperative Evaluation ◽

Curve Analysis ◽

Training Set ◽

Decision Curve Analysis ◽

Preoperative Prediction ◽

Mri Features ◽

Wide Range ◽

Validation Set

Abstract Background: Placenta previa is associated with higher percentage of intraoperative and postpartum hemorrhage, increased obstetric hysterectomy, significant maternal morbidity and mortality. We aimed to develop and validate a magnetic resonance imaging (MRI)-based nomogram to preoperative prediction of intraoperative hemorrhage (IPH) for placenta previa, which might contribute to adequate assessment and preoperative preparation for the obstetricians.Methods: Between May 2015 and December 2019, a total of 125 placenta previa pregnant women were divided into a training set (n = 80) and a validation set (n = 45). Radiomics features were extracted from MRI images of each patient. A MRI-based model comprising seven features was built for the classification of patients into IPH and non-IPH groups in a training set and validation set. Multivariate nomograms based on logistic regression analyses were built according to radiomics features. Receiver operating characteristic (ROC) curve was used to assess the model. Predictive accuracy of nomogram were assessed by calibration plots and decision curve analysis. Results: In multivariate analysis, placenta position, placenta thickness, cervical blood sinus and placental signals in the cervix were signifcantly independent predictors for IPH (all p < 0.05). The MRI-based nomogram showed favorable discrimination between IPH and non-IPH groups. The calibration curve showed good agreement between the estimated and the actual probability of IPH. Decision curve analysis also showed a high clinical benefit across a wide range of probability thresholds. The AUC was 0.918 ( 95% CI, 0.857-0.979 ) in the training set and 0.866( 95% CI, 0.748-0.985 ) in the validation set by the combination of four MRI features.Conclusions: The MRI-based nomograms might be a useful tool for the preoperative prediction of IPH outcomes for placenta previa. Our study enables obstetricians to perform adequate preoperative evaluation to minimize blood loss and reduce the rate of caesarean hysterectomy.

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S3437 Development of an Artificial Intelligence for Small Bowel Capsule Endoscopy Using Deep Learning

The American Journal of Gastroenterology ◽

10.14309/01.ajg.0000787280.20498.b5 ◽

2021 ◽

Vol 116 (1) ◽

pp. S1415-S1416

Author(s):

Naoki Hosoe ◽

Yukie Hayashi ◽

Kenji Limpias Kamiya ◽

Tomohisa Sujino ◽

Kaoru Takabayhashi ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Small Bowel ◽

Capsule Endoscopy

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Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Frontiers in Oncology ◽

10.3389/fonc.2021.701598 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yaoyao Zhuo ◽

Yi Zhan ◽

Zhiyong Zhang ◽

Fei Shan ◽

Jie Shen ◽

...

Keyword(s):

Clinical Features ◽

Ct Images ◽

Curve Analysis ◽

Good Prediction ◽

Training Set ◽

Minimum Diameter ◽

Decision Curve Analysis ◽

Thin Slice ◽

Solid Nodule ◽

Validation Set

AimTo investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN).Materials and MethodsA total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model.ResultsA total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99–1.00] and validation set (AUC, 0.99; 95% CI, 0.98–1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.ConclusionThe radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

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