Development and validation of a nomogram for distant metastasis in esophageal cancer based on radiomics and clinical factors.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16071-e16071
Author(s):  
Zhu Chao ◽  
Qingtao Qiu ◽  
Youxin Ji ◽  
Songping Wang ◽  
Jialin Ding ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Predictive Factors ◽  
Distant Metastasis ◽  
Machine Learning Algorithms ◽  
Lasso Regression ◽  
Clinical Factors ◽  
Net Reclassification Improvement ◽  
Contrast Enhanced Ct ◽  
Development And Validation ◽  
Radiomics Signature

e16071 Background: Distant metastasis with an incidence of 25% in esophageal cancer(EC) represents a poor prognosis. However, there was few study for prediction of distant metastasis in EC, due to unsatisfactory specificity of clinical factors and lack of reliable biomarkers. Methods: Two hundred and ninety-nine patients were enrolled and randomly assigned to a training cohort(n = 207) and a validation cohort(n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictive factors and construct a clinical nomogram. Radiomic features were extracted from contrast-enhanced CT performed before treatment, and Lasso regression was used to screen the optimal features, which were developed a radiomics signature subsequently. Four machine learning algorithms were used to establish radiomics models respectively based on the screened features. The joint nomogram incorporating radiomics signature and clinical independent predictors was developed by logical regression algorithm. All models were further validated by discrimination,caliberation, reclassification and clinical usefulness. Results: The joint nomogram had a better performance [AUC(95%CI): 0.827(0.742-0.912)] than clinical nomogram [AUC(95%CI): 0.731(0.626-0.836)]and radiomics predictive models[AUC(95%CI): 0.747(0.642-0.851),SVM algorithms]. Caliberation curve, and decision curve analysis also revealed joint nomogram outperformed the other models. Compared with the clinical nomogram, net reclassification Improvement(NRI) of the joint nomogram was improved by 0.114(0.075, 0.345),and integrated discrimination Improvement (IDI) was improved by 0.071(0.030-0.112), P= 0.001. Conclusions: We constructed and validated the first joint nomogram for distant metastasis in EC based on radiomics signature and clinical independent predictive factors, which could help clinicians to identify patients with high risk of distant metastasis.

Survival prediction in patients treated by FOLFIRI and bevacizumab for metastatic colorectal cancer (PRODIGE 9) using contrast-enhanced CT texture analysis (SPECTRA).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 3601-3601 ◽  
Author(s):  
Anthony Dohan ◽  
Benoit Gallix ◽  
Boris Guiu ◽  
Karine Le Malicot ◽  
Caroline Reinhold ◽  
...  
Keyword(s):  
Predictive Factors ◽  
Liver Lesion ◽  
Rank Test ◽  
Survival Prediction ◽  
Survival Status ◽  
Contrast Enhanced Ct ◽  
Morphological Criteria ◽  
Response To Chemotherapy ◽  
Patient Will ◽  
Radiomics Signature

3601 Background: Quantitative assessment of tumor architecture changes may help to early identify non-responder patients and propose a tailored treatment strategy. Our objective was to build and validate a radiomics signature able to predict early the lack of response to chemotherapy including FOLFIFRI and bevacizumab using baseline and first evaluation CT and to compare it to the RECIST and morphological criteria. Methods: For 230 patients of PRODIGE 9 study and treated by FOLFIRI and bevacizumab, a computed analysis (CA) was performed on the dominant liver lesion (DLL) at baseline and 2 months post-chemotherapy. RECIST evaluation was performed at 2 and 6 months. The sum of the target liver lesions (STL), the density of the DLL, CA parameters and their changes rates were correlated with the 2-year survival status. A radiomics signature combining 3 parameters was built in one arm and validated in the second arm. Survival was estimated with the Kaplan-Meier method and compared with log-rank test. Results: The strongest predictive factors for 2-year survival status were decrease in STL(AUC = .69±.05[95%CI:.60-.77]), change rate in kurtosis(ssf = 0) (AUC = .66±.05[95%CI:.57-.74]), and the baseline density of the DLL (AUC = .68±.05[95%CI:.59-.77]). Using multivariate analysis, predictive factors of 2-year survival status were the decrease in STL > 15%(HR = 1.92, P= .002), the increase in kurtosis value(ssf = 0) > 93% (HR = 2.16, P= .001), and baseline DLL > 64.3UH (HR = 1.70, P= .02). Then, the SPECTRA-score was built by according 1 point for each of the 3 criteria. Patients with a SPECTRA-score > 1 had a lower overall survival in the training ( P= .001) and in the validation cohort ( P= .002). Non-response according to RECIST at 6 months had the same prognostic value as SPECTRA-score>1 at 2 months. Conclusions: A radiomics signature combining STL, density and CA on baseline and first evaluation CT is be able to predict which patient will have a poor outcome with same performances than standard evaluation with RECIST1.1 at 6 months in mCRC patients. Clinical trial information: NCT00952029.

scholarly journals A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer

2021 ◽  
Author(s):  
Yumin Hu ◽  
Qiaoyou Weng ◽  
Haihong Xia ◽  
Tao Chen ◽  
Chunli Kong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Clinical Data ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Factors ◽  
Good Discrimination ◽  
Training Cohort ◽  
Ovarian Cancers ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

scholarly journals Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

2021 ◽  
Author(s):  
Nikita Sushentsev ◽  
Leonardo Rundo ◽  
Oleg Blyuss ◽  
Tatiana Nazarenko ◽  
Aleksandr Suvorov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Cancer Progression ◽  
Predictive Value ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Control Group ◽  
P Value ◽  
Lasso Regression

Abstract Objectives To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). Methods The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test. Results The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77). Conclusions PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. Key Points • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Development and validation of simplified machine learning algorithms to predict prognosis of hospitalized COVID-19 patients: a multi-center, retrospective study (Preprint)

2021 ◽  
Author(s):  
Fang He ◽  
John H Page ◽  
Kerry R Weinberg ◽  
Anirban Mishra
Keyword(s):  
Machine Learning ◽  
Respiratory Failure ◽  
High Risk ◽  
Learning Algorithms ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
High Risk Patients ◽  
Risk Patients ◽  
Development And Validation

BACKGROUND The current COVID-19 pandemic is unprecedented; under resource-constrained setting, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients, however there are few risk scores derived from a substantially large EHR dataset, using simplified predictors as input. OBJECTIVE To develop and validate simplified machine learning algorithms which predicts COVID-19 adverse outcomes, to evaluate the AUC (area under the receiver operating characteristic curve), sensitivity, specificity and calibration of the algorithms, to derive clinically meaningful thresholds. METHODS We conducted machine learning model development and validation via cohort study using multi-center, patient-level, longitudinal electronic health records (EHR) from Optum® COVID-19 database which provides anonymized, longitudinal EHR from across US. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, ICU admission, respiratory failure, mechanical ventilator usages at inpatient setting. Data from patients who were admitted prior to Sep 7, 2020, is randomly sampled into development, test and validation datasets; data collected from Sep 7, 2020 through Nov 15, 2020 was reserved as prospective validation dataset. RESULTS Of 3.7M patients in the analysis, a total of 585,867 patients were diagnosed or tested positive for SARS-CoV-2; and 50,703 adult patients were hospitalized with COVID-19 between Feb 1 and Nov 15, 2020. Among the study cohort (N=50,703), there were 6,204 deaths, 9,564 ICU admissions, 6,478 mechanically ventilated or EMCO patients and 25,169 patients developed ARDS or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC = 0.89 (0.89 - 0.89) on validation dataset (N=10,752)), consistent prediction through the second wave of pandemic from September to November (AUC = 0.85 (0.85 - 0.86) on post-development validation (N= 14,863)), great clinical relevance and utility. Besides, a comprehensive 386 input covariates from baseline and at admission was included in the analysis; the end-to-end pipeline automates feature selection and model development process, producing 10 key predictors as input such as age, blood urea nitrogen, oxygen saturation, which are both commonly measured and concordant with recognized risk factors for COVID-19. CONCLUSIONS The systematic approach and rigorous validations demonstrate consistent model performance to predict even beyond the time period of data collection, with satisfactory discriminatory power and great clinical utility. Overall, the study offers an accurate, validated and reliable prediction model based on only ten clinical features as a prognostic tool to stratifying COVID-19 patients into intermediate, high and very high-risk groups. This simple predictive tool could be shared with a wider healthcare community, to enable service as an early warning system to alert physicians of possible high-risk patients, or as a resource triaging tool to optimize healthcare resources. CLINICALTRIAL N/A

scholarly journals A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chaohua Zhu ◽  
Huixian Huang ◽  
Xu Liu ◽  
Hao Chen ◽  
Hailan Jiang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Nasopharyngeal Carcinoma ◽  
Local Recurrence ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Stable Model ◽  
Clinical Factors ◽  
Specificity And Sensitivity ◽  
Radiomics Signature

Purpose: We aimed to establish a nomogram model based on computed tomography (CT) imaging radiomic signature and clinical factors to predict the risk of local recurrence in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).Methods: This was a retrospective study consisting of 156 NPC patients treated with IMRT. Radiomics features were extracted from the gross tumor volume for nasopharynx (GTVnx) in pretreatment CT images for patients with or without local recurrence. Discriminative radiomics features were selected after t-test and the least absolute shrinkage and selection operator (LASSO) analysis. The most stable model was obtained to generate radiomics signature (Rad_Score) by using machine learning models including Logistic Regression, K-Nearest neighbor, Naive Bayes, Decision Tree, Stochastic Gradient Descent, Gradient Booting Tree and Linear Support Vector Classification. A nomogram for local recurrence was established based on Rad_Score and clinical factors. The predictive performance of nomogram was evaluated by discrimination ability and calibration ability. Decision Curve Analysis (DCA) was used to evaluate the clinical benefits of the multi-factor nomogram in predicting local recurrence after IMRT.Results: Local recurrence occurred in 42 patients. A total of 1,452 radiomics features were initially extracted and seven stable features finally selected after LASSO analysis were used for machine learning algorithm modeling to generate Rad_Score. The nomogram showed that the greater Rad_Score was associated with the higher risk of local recurrence. The concordance index, specificity and sensitivity in the training cohort were 0.931 (95%CI:0.8765–0.9856), 91.2 and 82.8%, respectively; whereas, in the validation cohort, they were 0.799 (95%CI: 0.6458–0.9515), 79.4, and 69.2%, respectively.Conclusion: The nomogram based on radiomics signature and clinical factors can predict the risk of local recurrence after IMRT in patients with NPC and provide evidence for early clinical intervention.

scholarly journals Radiomics Based on Thyroid Ultrasound Can Predict Distant Metastasis of Follicular Thyroid Carcinoma

2020 ◽  
Vol 9 (7) ◽  
pp. 2156
Author(s):  
Mi-ri Kwon ◽  
Jung Hee Shin ◽  
Hyunjin Park ◽  
Hwanho Cho ◽  
Eunjin Kim ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Distant Metastasis ◽  
Area Under The Curve ◽  
Distant Metastases ◽  
Support Vector ◽  
Svm Classifier ◽  
Gray Level ◽  
Thyroid Ultrasound ◽  
Widely Invasive ◽  
Radiomics Signature

We aimed to evaluate whether radiomics analysis based on gray-scale ultrasound (US) can predict distant metastasis of follicular thyroid cancer (FTC). We retrospectively included 35 consecutive FTCs with distant metastases and 134 FTCs without distant metastasis. We extracted a total of 60 radiomics features derived from the first order, shape, gray-level cooccurrence matrix, and gray-level size zone matrix features using US imaging. A radiomics signature was generated using the least absolute shrinkage and selection operator and was used to train a support vector machine (SVM) classifier in five-fold cross-validation. The SVM classifier showed an area under the curve (AUC) of 0.90 on average on the test folds. Age, size, widely invasive histology, extrathyroidal extension, lymph node metastases on pathology, nodule-in-nodule appearance, marked hypoechogenicity, and rim calcification on the US were significantly more frequent among FTCs with distant metastasis compared to those without metastasis (p < 0.05). Radiomics signature and widely invasive histology were significantly associated with distant metastasis on multivariate analysis (p < 0.01 and p = 0.003). The classifier using the results of the multivariate analysis showed an AUC of 0.93. The radiomics signature from thyroid ultrasound is an independent biomarker for noninvasively predicting distant metastasis of FTC.

scholarly journals Clinical results of multimodality therapy for esophageal cancer with distant metastasis

2018 ◽  
Vol 10 (3) ◽  
pp. 1500-1510 ◽  
Author(s):  
Masakuni Sakaguchi ◽  
Toshiya Maebayashi ◽  
Takuya Aizawa ◽  
Naoya Ishibashi ◽  
Tsutomu Saito
Keyword(s):  
Esophageal Cancer ◽  
Distant Metastasis ◽  
Clinical Results ◽  
Multimodality Therapy
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