Multimodal fusion workflow for target delineation in cardiac radioablation of ventricular tachycardia.

BACKGROUND: Cardiac radioablation (CR) is an innovative treatment to ablate cardiac arrythmia sources by radiation therapy. CR target delineation is a challenging task requiring the exploitation of highly different imaging modalities, including cardiac electro-anatomical mapping (EAM). PURPOSE: In this work, a data integration process is proposed to alleviate the tediousness of CR target delineation by generating a fused representation of the heart, including all the information of interest resulting from the analysis and registration of electro-anatomical data, PET scan and planning computed tomography (CT) scan. The proposed process was evaluated by cardiologists during delineation trials. METHODS: The data processing pipeline was composed of the following steps. The cardiac structures of interest were segmented from cardiac CT scans using a deep learning method. The EAM data was registered to the cardiac CT scan using a point cloud based registration method. The PET scan was registered using rigid image registration. The EAM and PET information, as well as the myocardium thickness, were projected on the surface of the 3D mesh of the left ventricle. The target was identified by delineating a path on this surface that was further projected to the thickness of the myocardium to create the target volume. This process was evaluated by comparison with a standard slice-by-slice delineation with mental EAM registration. Four cardiologists delineated targets for three patients using both methods. The variability of target volumes, and the ease of use of the proposed method, were evaluated. RESULTS: All cardiologists reported being more confident and efficient using the proposed method. The inter-clinician variability in delineated target volume was systematically lower with the proposed method (average dice score of 0.62 vs. 0.32 with a classical method). Delineation times were also improved. CONCLUSIONS: A data integration process was proposed and evaluated to fuse images of interest for CR target delineation. It effectively reduces the tediousness of CR target delineation, while improving inter-clinician agreement on target volumes. This study is still to be confirmed by including more clinicians and patient data to the experiments.

18F-FDG PET/CT-Based Prognostic Survival Model After Surgery for Head and Neck Cancer.

The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting overall survival (OS) and distant metastasis-free survival (DMFS) in a cohort of head and neck squamous cell carcinoma patients treated with surgery, to generate a prognostic model of OS and DMFS, and to validate this prognostic model with an independent cohort. Methods: A total of 382 consecutive patients with head and neck squamous cell carcinoma, divided into training (n = 318) and validation (n = 64) cohorts, were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and distance parameters for the primary tumor and lymph nodes defined by 2 segmentation methods (relative SUVmax threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The concordance index (c-index) was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and nodal MTV, with a c-index of 0.64 (P < 0.001). For DMFS, the significant parameters were T stage, nodal MTV, and maximal tumor-node distance, with a c-index of 0.76 (P < 0.001). These combinations of parameters were externally validated, with c-indices of 0.63 (P < 0.001) and 0.71 (P < 0.001) for OS and DMFS, respectively. Conclusion: The nodal MTV associated with the maximal tumor-node distance was significantly correlated with the risk of DMFS. Moreover, this parameter, in addition to clinical parameters, was associated with a higher risk of death. These prognostic factors may be used to tailor individualized treatment.

Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the sixth more frequent cancer worldwide. This type of cancer has a poor overall survival rate mainly due to underlying cirrhosis and risk of recurrence outside the treated lesion. Quantitative imaging within a radiomics workflow may help assessing the probability of survival and potentially may allow tailoring personalized treatments. In radiomics a large amount of features can be extracted, which may be correlated across a population and very often can be surrogates of the same physiopathology. This issues are more pronounced and difficult to tackle with imbalanced data. Feature selection strategies are therefore required to extract the most informative with the increased predictive capabilities. In this paper, we compared different unsupervised and supervised strategies for feature selection in presence of imbalanced data and optimize them within a machine learning framework. Multi-parametric Magnetic Resonance Images from 81 individuals (19 deceased) treated with stereotactic body radiation therapy (SBRT) for inoperable (HCC) were analyzed. Pre-selection of a reduced set of features based on Affinity Propagation clustering (non supervised) achieved a significant improvement in AUC compared to other approaches with and without feature pre-selection. By including the synthetic minority over-sampling technique (SMOTE) for imbalanced data and Random Forest classification this workflow emerges as an appealing feature selection strategy for survival prediction within radiomics studies.

Detection of bladder metabolic artifacts in (18)F-FDG PET imaging.

Positron emission tomography using (18)F-fluorodeoxyglucose ((18)F-FDG-PET) is a widely used imaging modality in oncology. It enables significant functional information to be included in analyses of anatomical data provided by other image modalities. Although PET offers high sensitivity in detecting suspected malignant metabolism, (18)F-FDG uptake is not tumor-specific and can also be fixed in surrounding healthy tissue, which may consequently be mistaken as cancerous. PET analyses may be particularly hampered in pelvic-located cancers by the bladder׳s physiological uptake potentially obliterating the tumor uptake. In this paper, we propose a novel method for detecting (18)F-FDG bladder artifacts based on a multi-feature double-step classification approach. Using two manually defined seeds (tumor and bladder), the method consists of a semi-automated double-step clustering strategy that simultaneously takes into consideration standard uptake values (SUV) on PET, Hounsfield values on computed tomography (CT), and the distance to the seeds. This method was performed on 52 PET/CT images from patients treated for locally advanced cervical cancer. Manual delineations of the bladder on CT images were used in order to evaluate bladder uptake detection capability. Tumor preservation was evaluated using a manual segmentation of the tumor, with a threshold of 42% of the maximal uptake within the tumor. Robustness was assessed by randomly selecting different initial seeds. The classification averages were 0.94±0.09 for sensitivity, 0.98±0.01 specificity, and 0.98±0.01 accuracy. These results suggest that this method is able to detect most (18)F-FDG bladder metabolism artifacts while preserving tumor uptake, and could thus be used as a pre-processing step for further non-parasitized PET analyses.

[Radiation therapy for chordomas and chondrosarcomas of the base of the skull and cervical spine]. / Irradiation par association de photons et de protons des chordomes et des chondrosarcomes de la base du crâne et du rachis cervical.

PURPOSE: Prospective analysis of local tumour control, survival and treatment complications in 67 consecutive patients treated with fractionated photon and proton radiation for a chordoma or a chondrosarcoma of the base of the skull and of the cervical spine. PATIENTS AND METHODS: Between December 1995 and January 2000, 67 patients with a median age of 52.3 years (14-85), were treated using 201 MeV proton beam of the centre de protonthérapie d'Orsay (CPO), 49 for a chordoma and 18 for a chondrosarcoma. Irradiation combined high-energy photons and protons. Photons represented 2/3 of the total dose and protons 1/3. The median total dose delivered within gross tumour volume was 67 Cobalt Gray Equivalent (CGE) (60-70). RESULTS: With a mean follow-up of 32 months (4-71), the 3-year local control rates were for chordomas and chondrosarcomas of 70.8% and 85.2%, respectively and 4-year overall survival rates of 87.7% and 75%, respectively. Fourteen tumours (21.5%) failed locally (eight within the gross tumor volume, four into the CTV and 2 in an unknown site). Seven patients died of tumour and one of intercurrent disease. In univariate analysis, age inférieur ou égal à 52.3 years (p = 0.002), maximum tumoral diameter < 44.7 mm (p = 0.02) and GTV < 28.4 mL (0.02), at time of radiotherapy, influenced positively the local control. According to multivariate analysis, only age was an independent prognostic factor of local control. Only five (7.7%) patients presented grade 3 or 4 complications. CONCLUSION: In base of skull chordomas and chondrosarcomas, the combined photon and proton therapy offers excellent chances of cure at the price of an acceptable toxicity. These results should be confirmed with a longer follow-up.