Quantitative diffusion-weighted MRI response assessment in rhabdomyosarcoma: an international retrospective study on behalf of the European paediatric Soft tissue sarcoma Study Group Imaging Committee

Biomarker; Diffusion magnetic resonance imaging; RhabdomyosarcomaBiomarcador; Imatge de ressonància magnètica de difusió; RabdomiosarcomaBiomarcador; Imágenes por resonancia magnética de difusión; RabdomiosarcomaObjective To investigate the feasibility of diffusion-weighted magnetic resonance imaging (DW-MRI) as a predictive imaging marker after neoadjuvant chemotherapy in patients with rhabdomyosarcoma. Material and methods We performed a multicenter retrospective study including pediatric, adolescent and young adult patients with rhabdomyosarcoma, Intergroup Rhabdomyosarcoma Study group III/IV, treated according to the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 or MTS2008 studies. DW-MRI was performed according to institutional protocols. We performed two-dimensional single-slice tumor delineation. Areas of necrosis or hemorrhage were delineated to be excluded in the primary analysis. Mean, median and 5th and 95th apparent diffusion coefficient (ADC) were extracted. Results Of 134 included patients, 82 had measurable tumor at diagnosis and response and DW-MRI scans of adequate quality and were included in the analysis. Technical heterogeneity in scan acquisition protocols and scanners was observed. Mean ADC at diagnosis was 1.1 (95% confidence interval [CI]: 1.1–1.2) (all ADC expressed in * 10−3 mm2/s), versus 1.6 (1.5–1.6) at response assessment. The 5th percentile ADC was 0.8 (0.7–0.9) at diagnosis and 1.1 (1.0–1.2) at response. Absolute change in mean ADC after neoadjuvant chemotherapy was 0.4 (0.3–0.5). Exploratory analyses for association between ADC and clinical parameters showed a significant difference in mean ADC at diagnosis for alveolar versus embryonal histology. Landmark analysis at nine weeks after the date of diagnosis showed no significant association (hazard ratio 1.3 [0.6–3.2]) between the mean ADC change and event-free survival. Conclusion A significant change in the 5th percentile and the mean ADC after chemotherapy was observed. Strong heterogeneity was identified in DW-MRI acquisition protocols between centers and in individual patients.R.E. and C.C. are funded by the KIKA Foundation (Children Cancer-free, number 357). H.M. acknowledges NHS funding from the NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research

Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC

IntroductionIn this study, we aim to build radiomics and multiomics models based on transcriptomics and radiomics to predict the response from patients treated with the PD-L1 inhibitor.Materials and methodsOne hundred and ninety-five patients treated with PD-1/PD-L1 inhibitors were included. For all patients, 342 radiomic features were extracted from pretreatment computed tomography scans. The training set was built with 110 patients treated at the Léon Bérard Cancer Center. An independent validation cohort was built with the 85 patients treated in Dijon. The two sets were dichotomized into two classes, patients with disease control and those considered non-responders, in order to predict the disease control at 3 months. Various models were trained with different feature selection methods, and different classifiers were evaluated to build the models. In a second exploratory step, we used transcriptomics to enrich the database and develop a multiomic signature of response to immunotherapy in a 54-patient subgroup. Finally, we considered the HOT/COLD status. We first trained a radiomic model to predict the HOT/COLD status and then prototyped a hybrid model integrating radiomics and the HOT/COLD status to predict the response to immunotherapy.ResultsRadiomic signature for 3 months’ progression-free survival (PFS) classification: The most predictive model had an area under the receiver operating characteristic curve (AUROC) of 0.94 on the training set and 0.65 on the external validation set. This model was obtained with the t-test selection method and with a support vector machine (SVM) classifier. Multiomic signature for PFS classification: The most predictive model had an AUROC of 0.95 on the training set and 0.99 on the validation set. Radiomic model to predict the HOT/COLD status: the most predictive model had an AUROC of 0.93 on the training set and 0.86 on the validation set. HOT/COLD radiomic hybrid model for PFS classification: the most predictive model had an AUROC of 0.93 on the training set and 0.90 on the validation set.ConclusionIn conclusion, radiomics could be used to predict the response to immunotherapy in non-small-cell lung cancer patients. The use of transcriptomics or the HOT/COLD status, together with radiomics, may improve the working of the prediction models

Tumoral Dissemination Along the Screw Trajectory in Percutaneous Osteosynthesis and Cementoplasty: A Non-described Complication

International audienc

Delayed Bronchocutaneous Fistula Without Pneumothorax Following a Microwave Ablation of a Recurrent Pulmonary Metastasis

International audiencePercutaneous tumor ablations are rather safe and effective treatments in selected patients for non-operable non-small cell lung carcinomas or lung metastases. However, there are major complications such as bronchopleural or bronchocutaneous fistula, which it is important to know in order to manage them safely. We describe in this report a case of bronchocutaneous fistula without pneumothorax following a microwave ablation of a recurrent pulmonary metastasis and its management

Descending thoracic aortic aneurysm revealing metastasis of a soft tissue fibrosarcoma: a case report and review of the literature

Abstract Background Review of the first documented case of aortic wall metastasis from a limb sarcoma. Case presentation In a 56-year-old woman with a diagnosis of a high-grade limb fibrosarcoma, an aortic metastasis was revealed by a fast growing aneurysm of the descending thoracic aorta. This was managed with an endoprosthesis. Conclusion The presence of an aneurysm in a patient with a sarcoma with a high potential for metastasis and poor cardiovascular risk factors should alert physicians

Prédiction de la réponse à la chimiothérapie des ostéosarcomes à partir des données radiomiques issues des IRM diagnostiques

International audienceIntroduction > L'ostéosarcome est la tumeur osseuse maligne la plus fréquente avant 25 ans. La réponse à la chimiothérapie néo-adjuvante influe la suite du traitement et est un facteur pronostique majeur. Il n'existe pas, actuellement, de moyens fiables de l'évaluer précocement. L'objectif de cette étude est de développer une méthode de prédiction de cette réponse à partir des données radiomiques issues de l'IRM diagnostique. Méthodes > Les caractéristiques cliniques et radiologiques de patients traités pour un ostéosar-come localisé ou métastatique en région Rhône-Alpes entre 2007 et 2016 ont été recueillies. Sur les IRM initiales, chaque tumeur était segmentée par un radiologue expert puis 87 caractéristiques radiomiques étaient extraites automatiquement. Une analyse univariée était réalisée pour évaluer l'association des caractéristiques avec la réponse histologique à la chimiothérapie néo-adjuvante. Pour distinguer les bons des mauvais répondeurs, nous avons construit des modèles prédictifs basés sur des machines à vecteurs de support. Leur performance de classification était évaluée par l'aire sous la courbe sensibilité/spécificité (AUROC). Résultats > L'analyse a porté sur les examens IRM de 69 patients dont 55,1 % (38/69) étaient bons répondeurs. Le modèle de prédiction obtenu à partir des données radiomiques issues de l'IRM diagnostique obtenait une aire sous la courbe de 0,98 avec une sensibilité de 100 % (IC 95 % [100 %-100 %]) et spécificité de 86 % (IC 95 % [59,7 %-111 %]). Discussion > Les caractéristiques radiomiques de l'IRM diagnostique pourraient permettre de prédire la réponse à la chimiothérapie des patients présentant un ostéosarcome avant de débuter la chimiothérapie néo-adjuvante

Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches

International audienceDetermining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER+ vs. ER−, PR+ vs. PR−, HER2+ vs. HER2−, and IDC vs. ILC classification tasks. The best results were obtained for the ER+ vs. ER− and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers

MRI-based radiomic to predict lipomatous soft tissue tumors malignancy

International audienceIn this study a MRI-based radiomic method was developed to predict lipomatous soft tissue tumors malignancy. 81 subjects with lipomatous soft tissue tumors whose histology was known and with fat-suppressed T1w contrast enhanced MR images available were retrospectively enrolled to constitute a database. A linear support vector machine was used after learning base dimension reduction to develop the model. Results demonstrate that the evaluation of lipomatous tumor malignancy is feasible with good diagnosis performances using a routinely used MRI acquisition in clinical practice

Percutaneous cryoablation of breast tumours in patients with stable metastatic breast cancer: safety, feasibility and efficacy

International audienc

MRI-based radiomics to predict lipomatous soft tissue tumors malignancy: a pilot study

International audienceObjectivesTo develop and validate a MRI-based radiomic method to predict malignancies in lipomatous soft tissue tumors.MethodsThis retrospective study searched in the database of our pathology department, data from patients with lipomatous soft tissue tumors, with histology and gadolinium-contrast enhanced T1w MR images, obtained from 56 centers with non-uniform protocols. For each tumor, 87 radiomic features were extracted by two independent observers to evaluate the inter-observer reproducibility. A reduction of learning base dimension was performed from reproducibility and relevancy criteria. A model was subsequently prototyped using a linear support vector machine to predict malignant lesions.ResultsEighty-one subjects with lipomatous soft tissue tumors including 40 lipomas and 41 atypical lipomatous tumors or well-differentiated liposarcomas with fat-suppressed T1w contrast enhanced MR images available were retrospectively enrolled. Based on a Pearson’s correlation coefficient threshold at 0.8, 55 out of 87 (63.2%) radiomic features were considered reproducible. Further introduction of relevancy finally selected 35 radiomic features to be integrated in the model. To predict malignant tumors, model diagnostic performances were as follow: AUROC = 0.96; sensitivity = 100%; specificity = 90%; positive predictive value = 90.9%; negative predictive value = 100% and overall accuracy = 95.0%.ConclusionThis work demonstrates that radiomics allows to predict malignancy in soft tissue lipomatous tumors with routinely used MR acquisition in clinical oncology. These encouraging results need to be further confirmed in an external validation population