CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

BACKGROUND AND PURPOSE: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. MATERIAL AND METHODS: We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). RESULTS: Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR < 5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77 × 10(−5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79 ×10(−17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56 × 10(−11)). CONCLUSIONS: Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data

Individualized early death and long-term survival prediction after stereotactic radiosurgery for brain metastases of non-small cell lung cancer:Two externally validated nomograms

Introduction Commonly used clinical models for survival prediction after stereotactic radiosurgery (SRS) for brain metastases (BMs) are limited by the lack of individual risk scores and disproportionate prognostic groups. In this study, two nomograms were developed to overcome these limitations. Methods 495 patients with BMs of NSCLC treated with SRS for a limited number of BMs in four Dutch radiation oncology centers were identified and divided in a training cohort (n = 214, patients treated in one hospital) and an external validation cohort n = 281, patients treated in three other hospitals). Using the training cohort, nomograms were developed for prediction of early death (<3 months) and long-term survival (>12 months) with prognostic factors for survival. Accuracy of prediction was defined as the area under the curve (AUC) by receiver operating characteristics analysis for prediction of early death and long term survival. The accuracy of the nomograms was also tested in the external validation cohort. Results Prognostic factors for survival were: WHO performance status, presence of extracranial metastases, age, GTV largest BM, and gender. Number of brain metastases and primary tumor control were not prognostic factors for survival. In the external validation cohort, the nomogram predicted early death statistically significantly better (p < 0.05) than the unfavorable groups of the RPA, DS-GPA, GGS, SIR, and Rades 2015 (AUC = 0.70 versus range AUCs = 0.51–0.60 respectively). With an AUC of 0.67, the other nomogram predicted 1 year survival statistically significantly better (p < 0.05) than the favorable groups of four models (range AUCs = 0.57–0.61), except for the SIR (AUC = 0.64, p = 0.34). The models are available on www.predictcancer.org. Conclusion The nomograms predicted early death and long-term survival more accurately than commonly used prognostic scores after SRS for a limited number of BMs of NSCLC. Moreover these nomograms enable individualized probability assessment and are easy into use in routine clinical practice

An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography.

peer reviewedPurpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza

[18F]FDG PET/CT-based response assessment of stage IV non-small cell lung cancer treated with paclitaxel-carboplatin-bevacizumab with or without nitroglycerin patches

Purpose Nitroglycerin (NTG) is a vasodilating drug, which increases tumor blood flow and consequently decreases hypoxia. Therefore, changes in [18F] fluorodeoxyglucose positron emission tomography ([18F] FDG PET) uptake pattern may occur. In this analysis, we investigated the feasibility of [18F] FDG PET for response assessment to paclitaxel-carboplatin-bevacizumab (PCB) treatment with and without NTG patches. And we compared the [18F] FDG PET response assessment to RECIST response assessment and survival. Methods A total of 223 stage IV non-small cell lung cancer (NSCLC) patients were included in a phase II study (NCT01171170) randomizing between PCB treatment with or without NTG patches. For 60 participating patients, a baseline and a second [18F] FDG PET/computed tomography (CT) scan, performed between day 22 and 24 after the start of treatment, were available. Tumor response was defined as a 30 % decrease in CT and PET parameters, and was compared to RECIST response at week 6. The predictive value of these assessments for progression free survival (PFS) and overall survival (OS) was assessed with and without NTG. Results A 30 % decrease in SUVpeak assessment identified more patients as responders compared to a 30 % decrease in CT diameter assessment (73 % vs. 18 %), however, this was not correlated to OS (SUVpeak30 p = 0.833; CTdiameter30 p = 0.557). Changes in PET parameters between the baseline and the second scan were not significantly different for the NTG group compared to the control group (p value range 0.159-0.634). The CT-based (part of the [18F] FDG PET/CT) parameters showed a significant difference between the baseline and the second scan for the NTG group compared to the control group (CT diameter decrease of 7 +/- 23 % vs. 19 +/- 14 %, p = 0.016, respectively). Conclusions The decrease in tumoral FDG uptake in advanced NSCLC patients treated with chemotherapy with and without NTG did not differ between both treatment arms. Early PET-based response assessment showed more tumor responders than CT-based response assessment (part of the [18F] FDG PET/CT); this was not correlated to survival. This might be due to timing of the [18F] FDG PET shortly after the bevacizumab infusion

Exploring Hypoxia in Prostate Cancer With T2-weighted Magnetic Resonance Imaging Radiomics and Pimonidazole Scoring

BACKGROUND/AIM: Radiomics involves high throughput extraction of mineable precise quantitative imaging features that serve as non-invasive prognostic or predictive biomarkers. High levels of hypoxia are associated with a poorer prognosis in prostate cancer and limit radiation therapy efficacy. Most patients with prostate cancer undergo magnetic resonance imaging (MRI) as a part of their diagnostics, and T2 imaging is the most utilised imaging method. The aim of this study was to determine whether hypoxia in prostate tumors could be identified using a radiomics model extracted from T2-weighted MR images.MATERIALS AND METHODS: Eighty eight intermediate or high-risk prostate cancer patients were evaluated. Prior to radical prostatectomy, all patients received pimonidazole (PIMO). PIMO hypoxic scores were assigned in whole-mount sections from prostatectomy specimens by an experienced pathologist who was blinded to MRI. The region of interest used for radiomics analysis included the prostatic index tumor. Radiomics extraction yielded 165 features using a special evaluation version of RadiomiX [RadiomiX Research Toolbox version 20180831 (OncoRadiomics SA, Liège, Belgium)] for non-clinical use. Multivariable logistic regression with Elastic Net regularization was utilised using 10 times repeated 10-fold cross-validation to select the best model hyperparameters, optimizing for area under the receiver operating characteristic curve (AUC).RESULTS: The average (out of sample) performance based on the repeated cross validation using the ONESE model yielded an AUC of 0.60±0.2. Shape-based features were the most prominent in the model.CONCLUSION: The development of a radiomics hypoxia model using T2 weighted MR images, standard in the staging of prostate cancer, is possible.</p