scholarly journals MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

2021 ◽  
Vol 11 ◽  
Author(s):  
Francesca Piludu ◽  
Simona Marzi ◽  
Marco Ravanelli ◽  
Raul Pellini ◽  
Renato Covello ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Malignant Tumors ◽  
Selection Process ◽  
Binary Classification ◽  
Treatment Plan ◽  
External Validation ◽  
Support Vector ◽  
Parotid Tumors ◽  
Apparent Diffusion ◽  
Malignant Lesions

BackgroundThe differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.Materials and MethodsA sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.ResultsModels for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.ConclusionsRadiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

scholarly journals MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Leporq ◽  
Amine Bouhamama ◽  
Frank Pilleul ◽  
Fabrice Lame ◽  
Catherine Bihane ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Predictive Value ◽  
Malignant Tumors ◽  
External Validation ◽  
Soft Tissue Tumors ◽  
Support Vector ◽  
Mr Images ◽  
Gadolinium Contrast ◽  
Contrast Enhanced ◽  
Lipomatous Tumors

Abstract Objectives To develop and validate a MRI-based radiomic method to predict malignancies in lipomatous soft tissue tumors. Methods This retrospective study searched in the database of our pathology department, data from patients with lipomatous soft tissue tumors, with histology and gadolinium-contrast enhanced T1w MR images, obtained from 56 centers with non-uniform protocols. For each tumor, 87 radiomic features were extracted by two independent observers to evaluate the inter-observer reproducibility. A reduction of learning base dimension was performed from reproducibility and relevancy criteria. A model was subsequently prototyped using a linear support vector machine to predict malignant lesions. Results Eighty-one subjects with lipomatous soft tissue tumors including 40 lipomas and 41 atypical lipomatous tumors or well-differentiated liposarcomas with fat-suppressed T1w contrast enhanced MR images available were retrospectively enrolled. Based on a Pearson’s correlation coefficient threshold at 0.8, 55 out of 87 (63.2%) radiomic features were considered reproducible. Further introduction of relevancy finally selected 35 radiomic features to be integrated in the model. To predict malignant tumors, model diagnostic performances were as follow: AUROC = 0.96; sensitivity = 100%; specificity = 90%; positive predictive value = 90.9%; negative predictive value = 100% and overall accuracy = 95.0%. Conclusion This work demonstrates that radiomics allows to predict malignancy in soft tissue lipomatous tumors with routinely used MR acquisition in clinical oncology. These encouraging results need to be further confirmed in an external validation population.

Plasma extracellular vesicle long RNA profiling identifies a diagnostic signature for the detection of pancreatic ductal adenocarcinoma

Gut ◽  
2019 ◽  
Vol 69 (3) ◽  
pp. 540-550 ◽  
Author(s):  
Shulin Yu ◽  
Yuchen Li ◽  
Zhuan Liao ◽  
Zheng Wang ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
External Validation ◽  
Extracellular Vesicle ◽  
Carbohydrate Antigen ◽  
Superior Performance ◽  
Ductal Adenocarcinoma ◽  
Support Vector ◽  
Internal Validation

ObjectivePancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose at resectable stage. Recent studies have suggested that extracellular vesicles (EVs) contain long RNAs. The aim of this study was to develop a diagnostic (d-)signature for the detection of PDAC based on EV long RNA (exLR) profiling.DesignWe conducted a case-control study with 501 participants, including 284 patients with PDAC, 100 patients with chronic pancreatitis (CP) and 117 healthy subjects. The exLR profile of plasma samples was analysed by exLR sequencing. The d-signature was identified using a support vector machine algorithm and a training cohort (n=188) and was validated using an internal validation cohort (n=135) and an external validation cohort (n=178).ResultsWe developed a d-signature that comprised eight exLRs, including FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2 and TIMP1, for PDAC detection. The d-signature showed high accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.960, 0.950 and 0.936 in the training, internal validation and external validation cohort, respectively. The d-signature was able to identify resectable stage I/II cancer with an AUC of 0.949 in the combined three cohorts. In addition, the d-signature showed superior performance to carbohydrate antigen 19-9 in distinguishing PDAC from CP (AUC 0.931 vs 0.873, p=0.028).ConclusionThis study is the first to characterise the plasma exLR profile in PDAC and to report an exLR signature for the detection of pancreatic cancer. This signature may improve the prognosis of patients who would have otherwise missed the curative treatment window.

scholarly journals Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yue Gao ◽  
Lingxi Chen ◽  
Jianhua Chi ◽  
Shaoqing Zeng ◽  
Xikang Feng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Illness ◽  
Prediction Model ◽  
Validation Cohort ◽  
External Validation ◽  
Validation Dataset ◽  
Support Vector ◽  
Internal Validation ◽  
Inflammatory Parameters ◽  
Severity Prediction

Abstract Background Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. Methods We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. Results Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979–1.000) in internal validation cohort and 0.999 (95% CI 0.998–1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. Conclusions The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. Trial registration This study was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2000032161). Graphical abstracthelper lymphocytve vv

scholarly journals Differential Diagnosis of Parotid Gland Tumors: Role of Shear Wave Elastography

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jan Heřman ◽  
Zuzana Sedláčková ◽  
Jaromír Vachutka ◽  
Tomáš Fürst ◽  
Richard Salzman ◽  
...  
Keyword(s):  
Parotid Gland ◽  
Shear Wave ◽  
Malignant Tumors ◽  
Shear Wave Elastography ◽  
Predictive Score ◽  
Cervical Lymph Nodes ◽  
Parotid Gland Tumors ◽  
Parotid Tumors ◽  
Malignant Lesions ◽  
Additional Value

Aim. To create a predictive score for the discrimination between benign and malignant parotid tumors using elastographic parameters and to compare its sensitivity and specificity with standard ultrasound. Methods. A total of 124 patients with parotid gland lesions for whom surgery was planned were examined using conventional ultrasound, Doppler examination, and shear wave elastography. Results of the examinations were compared with those ones of histology. Results. There were 96 benign and 28 malignant lesions in our cohort. Blurred tumor margin alone proved to be an excellent predictor of malignancy with the sensitivity of 79% and specificity of 97%. Enlarged cervical lymph nodes, tumor vascularisation, microcalcifications presence, homogeneous echogenicity, and bilateral occurrence also discriminated between benign and malignant tumors. However, their inclusion in a predictive model did not improve its performance. Elastographic parameters (the stiffness maxima and minima ratio being the best) also exhibited significant differences between benign and malignant tumors, but again, their inclusion did not significantly improve the predictive power of the blurred margin classifier. Conclusion. Even though elastography satisfactorily distinguishes benign from malignant lesions on its own, it hardly provides any additional value in evaluation of biological character of parotid gland tumors when used as an adjunct to regular ultrasound examination.

scholarly journals Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Chunli Li ◽  
Jiandong Yin
Keyword(s):  
Rectal Cancer ◽  
Diffusion Coefficient ◽  
Lymph Node ◽  
Apparent Diffusion Coefficient ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Support Vector ◽  
Training Cohort ◽  
Preoperative Prediction ◽  
Apparent Diffusion

PurposeTo develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.Materials and MethodsOne hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram.ResultsThe AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility.ConclusionsThe radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.

scholarly journals Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Yue Gao ◽  
Guang-Yao Cai ◽  
Wei Fang ◽  
Hua-Yi Li ◽  
Si-Yuan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Mortality Risk ◽  
Validation Cohort ◽  
External Validation ◽  
Warning System ◽  
Support Vector ◽  
Internal Validation ◽  
Machine Learning Methods ◽  
Boosted Decision Tree

Abstract Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Retrospective study on parotid gland tumors

ORL ro ◽  
2016 ◽  
Vol 4 (1) ◽  
pp. 22-23
Author(s):  
Ibric Cioranu ◽  
Vlad Petrescu Seceleanu ◽  
Viorel Ibric Cioranu ◽  
Andreea Smarandache ◽  
Sorin Vasilescu ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Maxillofacial Surgery ◽  
Squamous Carcinoma ◽  
Benign Tumors ◽  
Warthin Tumor ◽  
Parotid Tumors ◽  
Non Hodgkin Lymphoma ◽  
Adenoid Cystic ◽  
Modal Treatment ◽  
Surgery Department

During 2011-2012, 56 patients diagnosed with parotid tumors were admitted to the Maxillofacial Surgery Department of “Lucian Blaga” University and in Euroclinic Hospital. 72% were benign tumors and 28% malignant. All patients received surgical treatment (total or partial parotidectomy). For the malignant tumors, radiotherapy was added to the modal treatment (94% of the cases). Pleomorphic adenoma was encountered in 70% of the benign cases, followed by Warthin tumor in 15%. Adenoid cystic carcinoma was noticed in 31% of the malignant cases, mucoepidermoid carcinoma in 25% of the cases, and squamous carcinoma and non-Hodgkin lymphoma on 12.5% of the malignant cases.  

A prospective observational study on the effectiveness of risk of malignancy index score (RMI 3) in a tertiary care centre in Eastern India

2020 ◽  
Vol 11 (5) ◽  
pp. 54-60
Author(s):  
Apurba Mandal ◽  
Shibram Chattopadhyay ◽  
Sushanta Mondal ◽  
Arunava Biswas
Keyword(s):  
Ovarian Cancer ◽  
Adnexal Mass ◽  
Malignant Tumors ◽  
Histopathological Examination ◽  
Tertiary Care ◽  
Ca 125 ◽  
Tertiary Care Centre ◽  
Gynecology And Obstetrics ◽  
Risk Of Malignancy ◽  
Malignant Lesions

Background: Adnexal mass is a common presentation in today’s gynecological practice. The incidence of ovarian cancer is increasing day by day and diagnosis is often difficult to be made pre operatively with inadequate surgical exploration is a regular occurrence. Aims and Objectives: To assess and validate the importance of RMI-3 score as pre-operative diagnostic tool of differentiating benign from malignant adnexal mass for starting first line therapy of ovarian cancer and to find out the incidences of ovarian malignancy among study population. Material and Methods: The study was conducted in the Department of Gynecology and Obstetrics on (n=115) patients attending GOPD and indoor with adnexal mass fulfilling the inclusion and exclusion criteria using purposive sampling technique. All the selected cases underwent ultrasonography and serum CA- 125 level estimation necessary for calculating RMI score. A score of >200 was taken as suggestive of malignancy and confirmatory diagnosis was performed by histopathological examination obtained from staging laparotomy of adnexal mass. The individual scores were then correlated with final outcomes with statistical analyses. Results: The study revealed benign ovarian tumors are more under 50 years (78.46%) and patients with normal BMI are diagnosed with maximum of malignancy (n = 28). History of tubal ligation carried less risk of malignancy (p<0.0001). Histologically malignant tumors found mostly in 71.4% postmenopausal group whereas 94.1% benign pathology were present in perimenopausal group and there is no association found between parity and histopathology (p=0.058). Bilateral (p=0.013), multilocular (p=0.000) tumors with solid areas (p<0.0001) and thick papillary projections (p<0.0001) had statistically significant association with malignant lesions. RMI score (>200) had more efficacy than serum CA-125 level (>46) in differentiating malignant lesions from benign one in terms of specificity (96% vs 83.87%) and positive predictive value (95% vs 79.17%). Conclusions: RMI-3 score is a simple, reliable and effective tool in differentiating benign from malignant adnexal masses thereby help in quick referral and management of cases with increase chances of survival of the patients.

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking

2019 ◽  
Vol 15 (4) ◽  
pp. 328-340 ◽  
Author(s):  
Apilak Worachartcheewan ◽  
Napat Songtawee ◽  
Suphakit Siriwong ◽  
Supaluk Prachayasittikul ◽  
Chanin Nantasenamat ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Rational Design ◽  
External Validation ◽  
Rational Drug Design ◽  
Support Vector ◽  
Data Set ◽  
Qsar Models ◽  
Anti Hiv Agents ◽  
Anti Hiv ◽  
Colchicine Derivatives

Background: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. Objective: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. Methods: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. Results: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO−CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. Conclusion: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.

scholarly journals Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2133
Author(s):  
Francisco O. Cortés-Ibañez ◽  
Sunil Belur Nagaraj ◽  
Ludo Cornelissen ◽  
Gerjan J. Navis ◽  
Bert van der Vegt ◽  
...  
Keyword(s):  
Cancer Incidence ◽  
Binary Classification ◽  
Predictive Performance ◽  
Population Based ◽  
Support Vector ◽  
Clinical Variables ◽  
Incident Cancer ◽  
History Of ◽  
Diagnosis Of Cancer ◽  
Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

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