Antoine Iannessi

Computer tomography-based body surface area evaluation for drug dosage: Quantitative radiology versus anthropomorphic evaluation

Objective The measure of body surface area (BSA) is a standard for planning optimal dosing in oncology. This index is derived from a model having questionable performances. In this study, we proposed measurement of BSA from whole body CT images (iBSA). We tested the reliability of iBSA assessments and simulated the impact of our approach on patient chemotherapy dosage planning. Methods We first evaluated accuracy and precision of iBSA in measuring 14 phantom and 11 CT test-retest images.Secondly, we retrospectively analyzed 26 whole body PET-CT scans to evaluate inter-method variability between iBSA and the most used anthropomorphic models, notably the “Du Bois and Du Bois” model. Finally, we simulated the impact on chemotherapy dose planning of capecitabine based on iBSA. Results Precision and accuracy of iBSA measurement featured a standard deviation of 1.11% and a mean error of 1.53%. Inter-method variability between iBSA and “Du Bois and Du Bois” assessment featured a standard deviation of 4.11% leading to a reclassification rate of capecitabine of 32.5%. Conclusions iBSA could help the oncologist in standardizing assessments for chemotherapy planning. iBSA could also be relevant for applications such as comprehensive body composition and provide a sensitive measurement for changes related to nutritional intake or other metabolism.

Mutual information-based feature selection for radiomics

Background The extraction and analysis of image features (radiomics) is a promising field in the precision medicine era, with applications to prognosis, prediction, and response to treatment quantification. In this work, we present a mutual information – based method for quantifying reproducibility of features, a necessary step for qualification before their inclusion in big data systems. Materials and Methods Ten patients with Non-Small Cell Lung Cancer (NSCLC) lesions were followed over time (7 time points in average) with Computed Tomography (CT). Five observers segmented lesions by using a semi-automatic method and 27 features describing shape and intensity distribution were extracted. Inter-observer reproducibility was assessed by computing the multi-information (MI) of feature changes over time, and the variability of global extrema. Results The highest MI values were obtained for volume-based features (VBF). The lesion mass (M), surface to volume ratio (SVR) and volume (V) presented statistically significant higher values of MI than the rest of features. Within the same VBF group, SVR showed also the lowest variability of extrema. The correlation coefficient (CC) of feature values was unable to make a difference between features. Conclusions MI allowed to discriminate three features (M, SVR, and V) from the rest in a statistically significant manner. This result is consistent with the order obtained when sorting features by increasing values of extrema variability. MI is a promising alternative for selecting features to be considered as surrogate biomarkers in a precision medicine context.

Assessing the relevance of multi-planar MRI acquisitions for prostate segmentation using deep learning techniques

Background. Prostate segmentation is a crucial step in computer-aided systems for prostate cancer detection. Multi-planar acquisitions are commonly used by clinicians to obtain a more accurate patient diagnosis but their relevance in prostate segmentation using fully automated algorithms has not been assessed. To date, the limited assessment of this relevance stems from the fact that both axial and sagittal prostate imaging views, as opposed to a single view, doubles the acquisition time. In this work, we assess the relevance of multi-planar imaging for prostate segmentation within a deep learning segmentation framework. Materials and Methods. We propose a deep learning prostate segmentation framework either from either axial or from axial and sagittal T2-weighted magnetic resonance images (MRI). The system is based on an ensemble of convolutional neural networks, each independently trained on a single imaging view. We compare single-view (axial) segmentations to those obtained from two imaging views (axial and sagittal) to assess the relevance of using multi-planar acquisitions. Algorithm performance assessment will be two-fold: 1) the global DICE score between the algorithm’s predictions and the segmentations of an experienced reader will be computed and 2) the number of lesions located within the algorithm’s segmentation prediction will be calculated. A subset of 80 patients from the public PROSTATEx-2 database containing both axial and sagittal T2-weighted MRIs will be used for this study. Results. The multiplanar network outperformed the network trained on only axial views according to both the proposed metrics. A statistically significant increase of 4% in DICE scores was found along with an 9% increase in the number of lesions within the predicted segmentation. Conclusions. The proposed method allows for a fully automatic segmentation of the prostate from single- or multi-view MRI and assesses the relevance of multi-planar MRI acquisitions for fully automatic prostate segmentation algorithms.

How reliable are ADC measurements? A phantom and clinical study of cervical lymph nodes

ObjectiveTo assess the reliability of ADC measurements in vitro and in cervical lymph nodes of healthy volunteers.MethodsWe used a GE 1.5 T MRI scanner and a first ice-water phantom according to recommendations released by the Quantitative Imaging Biomarker Alliance (QIBA) for assessing ADC against reference values. We analysed the target size effect by using a second phantom made of six inserted spheres with diameters ranging from 10 to 37 mm. Thirteen healthy volunteers were also scanned to assess the inter- and intra-observer reproducibility of volumetric ADC measurements of cervical lymph nodes.ResultsOn the ice-water phantom, the error in ADC measurements was less than 4.3 %. The spatial bias due to the non-linearity of gradient fields was found to be 24 % at 8 cm from the isocentre. ADC measure reliability decreased when addressing small targets due to partial volume effects (up to 12.8 %). The mean ADC value of cervical lymph nodes was 0.87.10-3 ± 0.12.10-3 mm2/s with a good intra-observer reliability. Inter-observer reproducibility featured a bias of -5.5 % due to segmentation issues.ConclusionADC is a potentially important imaging biomarker in oncology; however, variability issues preclude its broader adoption. Reliable use of ADC requires technical advances and systematic quality control.Key Points• ADC is a promising quantitative imaging biomarker.• ADC has a fair inter-reader variability and good intra-reader variability.• Partial volume effect, post-processing software and non-linearity of scanners are limiting factors.• No threshold values for detecting cervical lymph node malignancy can be drawn.

Effects of relaxing therapies on patient’s pain during percutaneous interventional radiology procedures

BACKGROUND Interventional radiology procedures in cancer patients cause stress and anxiety. Our objective was to relate our experience in the use of sophrology techniques during interventional radiology procedures and evaluate the effects on patients pain and anxiety. METHODS We present a prospective observational study on 60 consecutive patients who underwent interventional radiology procedures in a context of oncologic management from September 2017 to March 2018. Forty-two patients were asked if they wished to benefit from the sophrology and hypnosis techniques during their procedure. A control group was also made including 18 patients. Anxiety level and pain were evaluated using the visual analog scale (VAS) before and during procedures. RESULTS We observed a significant decrease in anxiety experienced by patients during interventional radiology procedures compared to before procedures in the sophrology group (P=3.318E-08), and a level of anxiety and pain during gestures inferior to that of the control group (P=2.035E-06 and 7.03E-05 respectively). CONCLUSIONS Relaxing therapies, such as sophrology and hypnosis, seems to be an interesting additional tool for the management of patients in interventional oncology, inducing a decrease of stress, pain, and anxiety in patients.

Abstract 4106: Modified RECIST for monitoring hepatocellular carcinoma with computed tomography: Inter-reader variability of the response

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. In clinical trials, computer tomography (CT) is a widely used imaging technique for the monitoring of HCC patients, and modified RECIST (mRECIST) have been suggested as appropriate criteria in part because they distinguish the viable part from the necrotic part of the tumor. Based on longitudinal changes of tumor burden, this study investigates the causes of inter-reader variability in evaluating the therapeutic response of HCC when relying on mRECIST. 24 patients with advanced (unresectable and/or metastatic) HCC enrolled in a phase I/II multicentre international study were retrospectively reviewed. From the originally selected target lesions, 41 were randomly selected. Original mRECIST expert evaluations were aggregated to the retrospective readings of three radiologists having different expertise (non mRECIST experts). All the 150 readings performed by each readers were made in using an electronic calliper. Precision of measurements between couple of readers was analysed by assessing standard deviation (SD) and reproducibility coefficient (RDC). The agreement of readers responses were compared by using Kappa coefficient statistic. The variability between non-experts and the variability between mRECIST expert and non-experts were analysed. Reader9s disagreement at declaring progressive (PD) and responding (PR) patient, were visually classified between variability of the measure, difference in the perception of tumour boundaries and differences in using either RECIST or mRECIST criteria for a given lesion. SD of measurements between non-experts ranged [24.9%; 36.3%] and RDC was 16.6 [13.85; 23.95]. Kappa coefficients was 0.41 [0.28; 0.55]. SD of expert against non-experts ranged [33.2%; 41.1%] and RDC was 18.8 [16.02; 24.53]. Kappa coefficients was 0.20 [0.06; 0.35]. Pooling the four readers together, rate of discrepancy at declaring respectively PD or PR by patients was 47.8% (11/23) and 34.8% (8/23). 90.9% (10/11) of discrepancies at declaring PD were due to different perceptions of tumour boundaries. Discrepancy at declaring PR was, in 62.5% of the cases, correlated to the application of mRECIST, 25% was correlated to different perception of tumours boundaries. Inter-reader variability of HCC measurements was large which leads to poor agreement in the response. The main cause of discrepancy at declaring PD originated from the complexity of HCC patterns and the poor definition of tumours boundaries while discrepancies at detecting PR come from the variability of readers at selecting the viable part of the tumours. In the context of HCC clinical trials, reliability of endpoints depend on the criteria involved. Present study must be completed with an inter-reader analysis of the variability between mRECIST experts. Citation Format: hubert Beaumont, Sophie Egels, Antoine Iannessi, Eric Bonnard, Christopher Foley, Olivier Lucidarme. Modified RECIST for monitoring hepatocellular carcinoma with computed tomography: Inter-reader variability of the response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4106.