scholarly journals MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Jian Guo ◽  
Xiaolin Xu ◽  
Wenbin Wei ◽  
Junfang Xian
Keyword(s):  
Optic Nerve ◽  
Quantitative Imaging ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Visual Assessment ◽  
Recursive Feature Elimination ◽  
Imaging Features ◽  
Training Set ◽  
Nerve Invasion ◽  
Validation Set

Objectives: To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and compare its predictive performance with subjective radiologists’ assessment. Methods: We retrospectively enrolled 124 patients with pathologically proven RB (90 in training set and 34 in validation set) who had MRI scans before surgery. A radiomics model for predicting PLONI was developed by extracting quantitative imaging features from axial T2-weighted images and contrast-enhanced T1-weighted images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, whereupon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance in the training and validation set. The performance of the radiomics model was compared to radiologists’ assessment by DeLong test. Results: The AUC of the radiomics model for the prediction of PLONI was 0.928 in the training set and 0.841 in the validation set. Radiomics model produced better sensitivity than radiologists’ assessment (81.1% vs  43.2% in training set, 82.4vs 52.9% in validation set). In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists’ assessment was 0.674 (p < 0.001, DeLong test). Conclusion: MRI-based radiomics model to predict PLONI in RB patients was shown to be superior to visual assessment with improved sensitivity and AUC, may serve as a potential tool to guide personalized treatment.

scholarly journals MRI-based Radiomics Model Can Improve the Predictive Performance of Postlaminar Optic Nerve Invasion in Retinoblastoma

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Jian Guo ◽  
Xiaolin Xu ◽  
Wenbin Wei ◽  
Junfang Xian
Keyword(s):  
Optic Nerve ◽  
Quantitative Imaging ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Visual Assessment ◽  
Recursive Feature Elimination ◽  
Imaging Features ◽  
Training Set ◽  
Nerve Invasion ◽  
Validation Set

Abstract Purpose: To develop an MRI-based radiomics model to predict postlaminar optic nerve invasion (PLONI) in retinoblastoma (RB) and to compare its predictive performance with that of subjective radiologists’ assessment.Methods: We retrospectively enrolled 124 patients with pathologically proven RB (90 in the training set and 34 in the validation set) who had MRI scans before surgery in this retrospective study. A radiomics model for predicting PLONI was developed by extracting 2058 quantitative imaging features from axial T2-weighted images and contrast-enhanced T1-weighted images in the training set. The Kruskal-Wallis test, least absolute shrinkage and selection operator regression, and recursive feature elimination were used for feature selection, whereupon a radiomics model was built with a logistic regression (LR) classifier. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the accuracy were assessed to evaluate the predictive performance of PLONI in the training set and validation set. The performance of the radiomics model was compared to radiologists’ assessment.Results: The AUC of the radiomics model for the prediction of PLONI according to ROC analysis was 0.928 in the training set and 0.841 in the validation set. In all 124 patients, the AUC of the radiomics model was 0.897, while that of radiologists’ assessment was 0.674 (p< 0.001).Conclusions: By incorporating MRI-based radiomics features, we constructed a radiomics model to predict PLONI in patients with RB, and it was shown to be superior to visual assessment and may serve as a potential tool to guide personalized treatment.

scholarly journals Prediction of response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma using radiomic features

2020 ◽  
Author(s):  
Jian Jia ◽  
Lingwei Meng ◽  
Guidong Song ◽  
Shibin Sun ◽  
Chuzhong Li ◽  
...  
Keyword(s):  
Pituitary Adenoma ◽  
Stereotactic Radiotherapy ◽  
Quantitative Imaging ◽  
Area Under The Curve ◽  
Image Features ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Imaging Features ◽  
Operating Characteristics ◽  
Nonfunctioning Pituitary Adenoma

Abstract Background: For individually predicting preoperative response to Stereotactic radiotherapy for Nonfunctioning pituitary Adenoma with the use of a radiomics approach.Methods: 93 cases (training set: n = 62; test set: n = 31) were recruited with contrast-enhanced T1-weighted MRI (CE-T1) before stereotactic radiotherapy. All of these patients received another MRI scan to assess sensitivity of radiotherapy after 12 to 18 months. The shrinkage and no increase in tumor volume are regarded as sensitive to gamma knife radiotherapy. According to CE-T1 images, we extracted 1208 quantitative imaging features totally. Support vector machine (SVM) combined with recursive feature elimination (RFE) and grid-search trained a four-feature prediction mode verified with an assay of receiver operating characteristics (ROC) for an individual set of test. In addition, a ROC curves with individual feature and signature bar were constructed for prediction.Results: The cross-validation area under the curve (AUC) on the three-fold train set is 0.991,0.843 and 0.889. In terms of the test and training sets, T1-CE image features led to 0.897 and 0.914 AUC, separately. Conclusions: With the use of a radiomics method, the response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma was primarily predicted before the operation. The built mode performed well, suggesting that radiomics is promising to preoperatively predict sensitivity to radiotherapy in NFPA.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

scholarly journals A CT-based radiomics nomogram for predicting prognosis of coronavirus disease 2019 (COVID-19) radiomics nomogram predicting COVID-19

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200634
Author(s):  
Hang Chen ◽  
Ming Zeng ◽  
Xinglan Wang ◽  
Liping Su ◽  
Yuwei Xia ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Ct Images ◽  
Prediction Performance ◽  
Clinical Factors ◽  
Training Set ◽  
Clinical Model ◽  
Potential Biomarker ◽  
Validation Set

Objectives: To identify the value of radiomics method derived from CT images to predict prognosis in patients with COVID-19. Methods: A total of 40 patients with COVID-19 were enrolled in the study. Baseline clinical data, CT images, and laboratory testing results were collected from all patients. We defined that ROIs in the absorption group decreased in the density and scope in GGO, and ROIs in the progress group progressed to consolidation. A total of 180 ROIs from absorption group (n = 118) and consolidation group (n = 62) were randomly divided into a training set (n = 145) and a validation set (n = 35) (8:2). Radiomics features were extracted from CT images, and the radiomics-based models were built with three classifiers. A radiomics score (Rad-score) was calculated by a linear combination of selected features. The Rad-score and clinical factors were incorporated into the radiomics nomogram construction. The prediction performance of the clinical factors model and the radiomics nomogram for prognosis was estimated. Results: A total of 15 radiomics features with respective coefficients were calculated. The AUC values of radiomics models (kNN, SVM, and LR) were 0.88, 0.88, and 0.84, respectively, showing a good performance. The C-index of the clinical factors model was 0.82 [95% CI (0.75–0.88)] in the training set and 0.77 [95% CI (0.59–0.90)] in the validation set. The radiomics nomogram showed optimal prediction performance. In the training set, the C-index was 0.91 [95% CI (0.85–0.95)], and in the validation set, the C-index was 0.85 [95% CI (0.69–0.95)]. For the training set, the C-index of the radiomics nomogram was significantly higher than the clinical factors model (p = 0.0021). Decision curve analysis showed that radiomics nomogram outperformed the clinical model in terms of clinical usefulness. Conclusions: The radiomics nomogram based on CT images showed favorable prediction performance in the prognosis of COVID-19. The radiomics nomogram could be used as a potential biomarker for more accurate categorization of patients into different stages for clinical decision-making process. Advances in knowledge: Radiomics features based on chest CT images help clinicians to categorize the patients of COVID-19 into different stages. Radiomics nomogram based on CT images has favorable predictive performance in the prognosis of COVID-19. Radiomics act as a potential modality to supplement conventional medical examinations.

scholarly journals The role of red cell distribution width in the differential diagnosis of iron deficiency anemia and non-transfusion dependent thalassemia patients

2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Pokpong Piriyakhuntorn ◽  
Adisak Tantiworawit ◽  
Thanawat Rattanathammethee ◽  
Chatree Chai-Adisaksopha ◽  
Ekarat Rattarittamrong ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Microcytic Anemia ◽  
Deficiency Anemia ◽  
Operating Characteristics ◽  
Training Set ◽  
Distribution Width ◽  
Validation Set ◽  
Sensitivity Specificity

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy

scholarly journals Use of serum biomarkers in staging of canine hepatic fibrosis

2019 ◽  
Vol 31 (5) ◽  
pp. 665-673 ◽  
Author(s):  
Maud Menard ◽  
Alexis Lecoindre ◽  
Jean-Luc Cadoré ◽  
Michèle Chevallier ◽  
Aurélie Pagnon ◽  
...  
Keyword(s):  
Liver Biopsy ◽  
Hepatic Fibrosis ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Gamma Glutamyl Transferase ◽  
Training Set ◽  
Internal Validation ◽  
Validation Set ◽  
Glutamyl Transferase

Accurate staging of hepatic fibrosis (HF) is important for treatment and prognosis of canine chronic hepatitis. HF scores are used in human medicine to indirectly stage and monitor HF, decreasing the need for liver biopsy. We developed a canine HF score to screen for moderate or greater HF. We included 96 dogs in our study, including 5 healthy dogs. A liver biopsy for histologic examination and a biochemistry profile were performed on all dogs. The dogs were randomly split into a training set of 58 dogs and a validation set of 38 dogs. A HF score that included alanine aminotransferase, alkaline phosphatase, total bilirubin, potassium, and gamma-glutamyl transferase was developed in the training set. Model performance was confirmed using the internal validation set, and was similar to the performance in the training set. The overall sensitivity and specificity for the study group were 80% and 70% respectively, with an area under the curve of 0.80 (0.71–0.90). This HF score could be used for indirect diagnosis of canine HF when biochemistry panels are performed on the Konelab 30i (Thermo Scientific), using reagents as in our study. External validation is required to determine if the score is sufficiently robust to utilize biochemical results measured in other laboratories with different instruments and methodologies.

scholarly journals Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiao-Hui Wang ◽  
Xiaopan Xu ◽  
Zhi Ao ◽  
Jun Duan ◽  
Xiaoli Han ◽  
...  
Keyword(s):  
Model Development ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Recursive Feature Elimination ◽  
Group Differences ◽  
Support Vector ◽  
Nasopharyngeal Swab ◽  
Feature Subset ◽  
Optimal Subset ◽  
Discharged Patients

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols.Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12–14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine–based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development.Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p &lt; 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case.Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

Identification of predictive biomarkers of overall survival (OS) in patients (pts) with advanced renal cell carcinoma (RCC) treated with interferon alpha (I) with or without bevacizumab (B): Results from CALGB 90206 (Alliance).

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4520-4520 ◽  
Author(s):  
Andrew B. Nixon ◽  
Susan Halabi ◽  
Ivo Shterev ◽  
Mark Starr ◽  
John C Brady ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Risk Groups ◽  
Phase Iii ◽  
Plasma Samples ◽  
Training Set ◽  
Validation Set

4520 Background: CALGB 90206 was a phase III trial of 732 pts with RCC comparing B+I versus I alone demonstrating no difference in OS. To date, there are no validated predictive biomarkers for B in RCC. For this reason, baseline plasma samples from CALGB 90206 pts were analyzed to identify and test predictive markers for B+I in RCC pts. Methods: Baseline EDTA plasma samples from 424 consenting pts were analyzed using an optimized multiplex ELISA platform for 32 candidate factors related to tumor growth, angiogenesis, and inflammation. The data were randomly split into training (n=286) and validation (n=138) sets. The proportional hazards model was used to test for treatment-marker interactions of OS. The estimated coefficients from the training set were used to compute a risk score (RS) for each pt in the validation set. The RS classified pts by risk in the validation set. The model was assessed for its predictive accuracy using area under the curve (AUC). Results: A statistically significant 3-way interaction between interleukin-6 (IL-6), hepatocyte growth factor (HGF) and treatment was observed in the training set (p<0.0001). The median levels of IL-6 and HGF in the training set were 8.4 pg/ml and 89 pg/ml, respectively. In the validation set, the RS was predictive of OS (p<0.001) with the high and low risk groups having a median OS of 10 months and 32 months, respectively. The AUC in the validation set was 0.82 (95% CI=0.77-0.88). The median OS (in months) by median levels of IL-6 and HGF stratified by treatment arm in the validation set is presented in the table with associated 95% CI (NR=not reached). Conclusions: IL-6 and HGF are predictive for OS in RCC patients treated with B+I and a RS based on these factors identified patients who benefitted most from B. If independently validated, this novel RS could guide clinical decisions and pt selection in future RCC trials. [Table: see text]

Combining molecular and functional imaging in metastatic castration-resistant prostate cancer (mCRPC).

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 141-141
Author(s):  
Vincenza Conteduca ◽  
Emanuela Scarpi ◽  
Daniel Wetterskog ◽  
Paola Caroli ◽  
Alessandro Romanel ◽  
...  
Keyword(s):  
Functional Imaging ◽  
Risk Group ◽  
Prognostic Score ◽  
Risk Groups ◽  
Imaging Features ◽  
Training Set ◽  
Group Iii ◽  
Group I ◽  
Group Ii ◽  
Validation Set

141 Background: Recently, plasma tumour DNA (ptDNA) has been identified as a potential early noninvasive biomarker of treatment response in mCRPC patients ( Conteduca, Br J Cancer 2020). In this study, we sought to determine whether pre-treatment ptDNA could accurately reflect metabolic tumor burden in mCRPC and if it could be in combination with functional imaging could provide better prognostication. Methods: Between October 2011 and June 2016, 102 plasma samples from mCRPC patients treated with abiraterone or enzalutamide were collected. Targeted next-generation sequencing was performed to determine baseline ptDNA fraction. Maximum standardized uptake value (SUVmax), total lesion activity (TLA), and metabolic tumour volume (MTV) were calculated on 18F-fluorocholine positron emission tomography/computed tomography. A Weibull multiple regression model was adopted to evaluate the combined impact of clinical, molecular and imaging features on overall survival (OS) and to obtain a prognostic score. Each variable was allotted a “partial score” that depended on the size of the regression coefficient. Total scores ranged between 0 and 5.85 and assigned patients to 3 different risk groups according to 18-months survival probability: group I, >70%; group II 30%-70%; and group III, <30%. We estimated OS probabilities by the exponential model and by the Kaplan-Meier method. Results: We observed a significant association between ptDNA levels dichotomized as below or above median plasma tumor fraction (low ptDNA≤0.188 versus high ptDNA>0.188) and median SUVmax (p<0.0001), MTV (p=0.0005) and TLA (p<0.0001). Patients were randomly divided into a training set (n=68) and a validation set (n=34). In the training cohort, we performed a multivariable analysis showing that visceral metastasis, serum LDH, MTV and ptDNA were independent predictors of OS [HR=2.64, 95%CI 1.32-5.26, p=0.006; HR=3.69, 95%CI 1.98-6.87, p<0.0001; HR=1.91, 95%CI 1.13-3.21, p=0.015; and HR=2.64, 95%CI 1.32-5.26, p=0.003, respectively]. In the training set, median OS was significantly different among the 3 risk groups (risk group I, 29.2 months [95% CI, 18.3 to 37.0 months]; risk group II, 15.9 months [95% CI, 10.6 to 24.0 months]; and risk group III, 8.7 months [95% CI, 6.3 to 15.4 months]; p<0.0001). Similar results were observed in the validation set groups (risk group I, 23.4 months [95% CI, 8.1 to 38.5 months]; risk group II, 13.3 months [95% CI, 3.7 to 18.0 months]; and risk group III, 7.3 months [95% CI, 2.6 to 11.8 months]; p=0.001). Conclusions: Integrating plasma DNA analysis with functional imaging may improve prognostic risk stratification and treatment selection in mCRPC patients. A larger prospective evaluation is now warranted.

Radiomic signatures to predict survival in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib +/- doxorubicin: Correlative science from CALGB 80802 (Alliance).

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 343-343
Author(s):  
Laurent Dercle ◽  
Susan Michelle Geyer ◽  
Andrew B. Nixon ◽  
Federico Innocenti ◽  
Qian Shi ◽  
...  
Keyword(s):  
Error Rate ◽  
Tumor Volume ◽  
Quantitative Imaging ◽  
Measurement Techniques ◽  
Tumor Burden ◽  
Phase Iii ◽  
Advanced Hepatocellular Carcinoma ◽  
Imaging Features ◽  
Time Points ◽  
Validation Set

343 Background: Alliance/CALGB 80802, a randomized phase III trial, evaluated sorafenib plus doxorubicin vs. doxorubicin in pts with HCC and showed no improvement in median overall survival (OS) (HR[95CI] 1.05[0.83-1.31]) or PFS (HR[95CI] 0.93[0.75-1.16]). In HCC surrogacy of tumor response with OS remains controversial, in part due to varying criteria used for response evaluation (e.g., RECIST1.1 and mRECIST). We evaluated the performance of several models to predict OS using pretreatment clinical and radiomic variables. Methods: In CALBG 80802, we segmented all measurable tumor lesions on sequential CT scans. A lesion’s imaging phenotype was deciphered with 23 uncorrelated quantitative imaging features measured at baseline and week (wk) 10 (first follow-up). An OS landmark survival analysis was conducted at wk 10. Patients were randomly assigned (3:1) to training (n = 92) and validation (n = 37) sets. In a training set, 6 random forest predictive models (6 signatures) used features that best predicted OS using 3 sets of variables: radiomics only (n = 23), clinical only (n = 9), radiomics and clinical (n = 32). Two time points (baseline only or baseline + wk 10) were assessed. Each signature's output was an individualized prediction and a continuous value ranging from 0 to 1 (from most to least favorable predicted OS). The primary endpoint was to compare these models' performance to predict OS using error rate (Harrell's concordance-index) in the validation set. Results: Of the 6 training signatures evaluated, the one achieving the highest performance in the validation set was an 8-feature signature combining radiomics and clinical variables measured at two time points (baseline + wk 10) with an error rate of 35.6%. The variables [rank of importance] (table) selected by the signature included baseline clinical features (albumin[1], AFP[2], Child-Pugh[4]), baseline radiomics features (component 17[3], component 1[5], component 9[7], tumor volume[8]) and wk 10 radiomics features (delta tumor volume[6]). Variable delta tumor volume [6] used a more enhanced estimation of tumor burden at baseline and a delta tumor volumetric measurement; compared to RECIST1.1 measurement of percentage change in unidimensional measurement of a subset of target lesions. The four quartiles of the signature were significantly associated with OS (Log-Rank, P < 0.0001). Conclusions: The selected combined radiomic and clinical composite signature provided the best prediction for OS in the 80802 study patients’ population. It is a suggested way forward to go beyond single anatomic measurement techniques such as RECIST or mRECIST. [Table: see text]

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