Supervoxel based 3D diseased lung segmentation

Computer-Aided Diagnosis relies on accurate tumor volume and heterogeneity assessment through CT-scans. Precise lesion segmentation is essential for patient diagnosis, therefore the development of automatic tools for lesion segmentation is needed. To improve lung nodule segmentation performance, lung segmentation masks serve as valuable priors, narrowing the focus to lung regions. Current methods suffer from the exclusion of pathological areas, especially in oncology patients, since tumor tissue differ in voxel density from other structures in the lung. Consequently, ensuring accurate lung segmentation encompassing all lesions is crucial. We developed a method based on supervoxels to fully segment the lung while encompassing nodules if present using a propagation algorithm based on geometrical properties. We compared our method to a morphology based method and neural networks trained to segment the lungs. Our method had the best performance in the inclusion of lung lesions, while retaining an adequate level of precision

3D lung nodule segmentation from 2D annotations using morphological operations

International audienceTumor volume and heterogeneity are important for patient diagnosis, and automatic lesion segmentation is needed to compute this information from routine CT-Scans. Training a supervised neural network to solve these tasks demands good quality annotations on a large quantity of fully annotated scans, which are difficult and time-consuming to obtain. We propose a fast automatic method using morphological operators to create 3D masks from hand drawn contours of the lesions on their largest axial slice. This type of annotation leads to more precise 3D masks than points or ellipses. Thus, the obtained mask may be used to train end-to-end neural networks for detection and semantic segmentation of lesions on CT-Scans in 3D. We tested this methodology on the LIDC-LUNA dataset to produce the 3D masks from automatically selected 2D annotations. We also produced 3D masks of 115 lung lesions from their 2D contours, and compared them to ground truth 3D masks on an in-house dataset. The results are promising, and the method could be adapted to other organs

Supervoxel based 3D diseased lung segmentation

Computer-Aided Diagnosis relies on accurate tumor volume and heterogeneity assessment through CT-scans. Precise lesion segmentation is essential for patient diagnosis, therefore the development of automatic tools for lesion segmentation is needed. To improve lung nodule segmentation performance, lung segmentation masks serve as valuable priors, narrowing the focus to lung regions. Current methods suffer from the exclusion of pathological areas, especially in oncology patients, since tumor tissue differ in voxel density from other structures in the lung. Consequently, ensuring accurate lung segmentation encompassing all lesions is crucial. We developed a method based on supervoxels to fully segment the lung while encompassing nodules if present using a propagation algorithm based on geometrical properties. We compared our method to a morphology based method and neural networks trained to segment the lungs. Our method had the best performance in the inclusion of lung lesions, while retaining an adequate level of precision

3D lung nodule segmentation from 2D annotations using morphological operations

International audienceTumor volume and heterogeneity are important for patient diagnosis, and automatic lesion segmentation is needed to compute this information from routine CT-Scans. Training a supervised neural network to solve these tasks demands good quality annotations on a large quantity of fully annotated scans, which are difficult and time-consuming to obtain. We propose a fast automatic method using morphological operators to create 3D masks from hand drawn contours of the lesions on their largest axial slice. This type of annotation leads to more precise 3D masks than points or ellipses. Thus, the obtained mask may be used to train end-to-end neural networks for detection and semantic segmentation of lesions on CT-Scans in 3D. We tested this methodology on the LIDC-LUNA dataset to produce the 3D masks from automatically selected 2D annotations. We also produced 3D masks of 115 lung lesions from their 2D contours, and compared them to ground truth 3D masks on an in-house dataset. The results are promising, and the method could be adapted to other organs

Prediction of Early Response to Immunotherapy: DCE-US as a New Biomarker

International audiencePurpose: The objective of our study is to propose fast, cost-effective, convenient, and effective biomarkers using the perfusion parameters from dynamic contrast-enhanced ultrasound (DCE-US) for the evaluation of immune checkpoint inhibitors (ICI) early response. Methods: The retrospective cohort used in this study included 63 patients with metastatic cancer eligible for immunotherapy. DCE-US was performed at baseline, day 8 (D8), and day 21 (D21) after treatment onset. A tumor perfusion curve was modeled on these three dates, and change in the seven perfusion parameters was measured between baseline, D8, and D21. These perfusion parameters were studied to show the impact of their variation on the overall survival (OS). Results: After the removal of missing or suboptimal DCE-US, the Baseline-D8, the Baseline-D21, and the D8-D21 groups included 37, 53, and 33 patients, respectively. A decrease of more than 45% in the area under the perfusion curve (AUC) between baseline and D21 was significantly associated with better OS (p = 0.0114). A decrease of any amount in the AUC between D8 and D21 was also significantly associated with better OS (p = 0.0370). Conclusion: AUC from DCE-US looks to be a promising new biomarker for fast, effective, and convenient immunotherapy response evaluation

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters’ predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan–Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts

Better than RECIST and faster than iRECIST: defining the Immunotherapy Progression Decision score to better manage progressive tumors on immunotherapy

International audienc

Better than RECIST and faster than iRECIST: defining the Immunotherapy Progression Decision score to better manage progressive tumors on immunotherapy

International audienc

1022MO Predicting overall survival of patients with melanoma and NSCLC treated with immunotherapy using AI combining total tumor volume and tumor heterogeneity on CT-Scans

International audienceUsing total tumor volume and AI tumor heterogeneity from CT scans, it may be possible to predict the prognosis of oncology patients, identify those who could benefit from immunotherapy, and provide valuable guidance for treatment

Better than RECIST and faster than iRECIST: defining the Immunotherapy Progression Decision score to better manage progressive tumors on immunotherapy

International audienc