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Background Only a fraction of patients with metastatic melanoma derive durable benefit from approved treatments. The clinical impact of personalized medicine strategies for melanoma, apart from BRAF , NRAS , or CKIT targeting, has rarely been reported. Materials and Methods By means of the Group of Cutaneous Oncology of the French Society of Dermatology, we retrospectively included all patients with advanced melanoma aged 18 years and older for whom molecular testing identified one or more actionable molecular alterations and who accordingly received molecularly matched therapy. We excluded patients with only BRAF , NRAS , or CKIT alterations and patients who received molecularly matched therapy for less than 15 days. Results We included 26 patients with a median follow-up of 8 months (1-54), a median age of 63 years (24-89), and a sex ratio of 2.7. These patients had been heavily pretreated, and 64% had elevated LDH levels. The disease control rate was 38%, with 4 cases of partial response (overall response rate: 15%) and 6 of stable disease for at least 6 months. The median duration of treatment was 3.1 months (0.9-13.5). Among patients with disease control, the median duration of control was 6.6 months (2.6-13.5) and 3 cases were ongoing at the end of the study. Patients with controlled disease had GNA11 , MAP2K1 , FYCO1-RAF1 , HRAS , ATM , CCND1 , MDM2/CDK4 , and CDKN2A/NRAS alterations. Conclusions High-throughput sequencing followed by matched targeted therapy is a promising approach for patients with advanced melanoma refractory to approved treatments. [ABSTRACT FROM AUTHOR]

Objectives: To assess the ability of the aortic aneurysm volume (AAV), aneurysmal lumen volume (ALV), and aneurysmal thrombus volume (ATV) to predict the need for aortic reintervention when using the maximal aortic diameter as a reference. Methods: This monocentric retrospective study included 31 consecutive patients who underwent successful thoracic endovascular aortic repair (TEVAR) to treat an atheromatous thoracic aortic aneurysm. All patients underwent clinical and computed tomography angiography (CTA) for 3 years after TEVAR. The patients were categorized into group 0 if no aortic reintervention was required during the follow-up period and categorized into group 1 if they experienced a type I or III endoleak or aneurysm diameter increase requiring intervention. The maximum aneurysm sac diameter and the AAV, ALV, and ATV were calculated using CTA images obtained preoperatively (T0) and at 6–12 months (T1), 24 months (T2), and 36 months (T3) postoperatively, and their changes over time were analyzed. Correlations between diameter and changes in AAV, ALV, and ATV were assessed, and the association between diameter and volume changes and reintervetion was examined. The cutoff values for predicting the need for reintervention was determined using a receiver operating characteristic (ROC) curve. The accuracy of volume change versus diameter change for predicting the need for reintervention was analyzed. Results: There were no significant differences in terms of the mean aneurysm diameter or AAV, ALV or ATV between the groups at preoperative CTA or after one year of follow-up imaging. The mean ATV was higher in group 1 than in group 0 at 2 years (187.6 ± 86.3 mL vs. 114.7 ± 64.7 mL; p = 0.057) and after 3 years (195.0 ± 86.7 mL vs. 82.1 ± 39.9 mL; p = 0.013). The maximal diameter was greater in group 1 than in group 0 at 3 years (67.3 ± 9.5 mm vs. 55.3 ± 12.6 mm; p = 0.044). The rate of AAV change between T0 and T1 was significantly higher in group 1 (7 ± 4.5%) than in group 0 (−6 ± 6.8%; p < 0.001). The rate of ATV change between T1-T3 was significantly higher in group 1 than in group 0 (34 ± 40.9% vs. −13 ± 14.4% (p = 0.041)); similar results were observed for the rate of ATV change between T2 and T3 (27 ± 50.1% for group 1 vs. −8 ± 49.5% in group 0 (p < 0.001)). According to our multivariate analysis, the annual growth rate for AAV between T0 and T1 was the only independent factor that was significantly associated with aortic reintervention (area under the curve (AUC) = 0.84, OR = 1.57, p = 0.025; optimal cutoff +0.4%). An increase in the annual growth rate of the ATV between T0 and T3 was independently associated with the need for aortic reintervention (area under the curve (AUC) = 0.90, OR = 1.11, p = 0.0347; optimal cutoff +10.1%). Conclusions: Aortic volume analysis can predict the need for aortic reintervention more accurately and earlier than maximal aortic diameter. [ABSTRACT FROM AUTHOR]

Desmoid tumors (DT) are rare benign tumors with a local invasion potential and recurrence. It is characterized on histology by an abnormal fibroblastic proliferation in a collagenous stroma, in variable proportions leading to heterogeneity of the lesion signal on magnetic resonance imaging (MRI). Current guidelines propose watchful waiting but in case of progression or symptoms, cryotherapy may be a therapeutic option in its extra-abdominal form. Tumor recurrence is mostly detected based on post-contrast magnetic resonance imaging (MRI). Although DWI sequence is the key-sequence for tumor detection in oncologic imaging, there are very few data in literature on diffusion weighted imaging (DWI) in DT generally and even fewer on DT after cryotherapy. DWI changes after cryotherapy may be confusing and suspicious of residual tumor or tumor recurrence when displaying low ADC values; thus knowledge of possible DWI patterns after cryotherapy of DT seem paramount. We found that the early changes of DT after cryotherapy are hyperintensity on DWI sequence with low ADC values (<1.00 × 10−3mm2/s), without corresponding enhancement and a later decrease in signal of the treated lesion on DWI. The freezing-thawing cycles of cryotherapy turn DT into gelatinous necrosis with a slow resorption rate, as reported in the only few studies referring of changes of DWI signals after cryotherapy, which are on renal and prostate models. Hyperintensity on DWI with low ADC values may be seen in early MRI follow-up after cryotherapy of extra-abdominal DT, corresponding with tumor necrosis changes and should not be mistaken with recurrence. Magnetic resonance imaging is the modality of choice for desmoid tumor (DT) follow-up, mainly based on contrast uptake which make data on diffusion weighted imaging (DWI) very rare. Cryotherapy is an accepted therapeutic option for DT that will lead to tumor necrosis. Hyperintensity on DWI with low apparent diffusion coefficient values is a possible expected early pattern on DWI after cryotherapy of DT. [ABSTRACT FROM AUTHOR]

In this study, we report our local experience of type A aortic dissections in patients with cerebral malperfusion treated with carotid stenting before or after aortic surgery, and present a systematic literature review on these patients treated either with carotid stenting (CS) before or after aortic surgery (AS) or with aortic and carotid surgery alone (ACS). We report on patients treated in our center with carotid stenting for brain hemodynamic injury of carotid origin caused by type A dissection since 2018, and a systematic review was conducted in PubMed for articles published from 1990 to 2021. Out of 5307 articles, 19 articles could be included with a total of 80 patients analyzed: 9 from our center, 29 patients from case reports, and 51 patients from two retrospective cohorts. In total, 8 patients were treated by stenting first, 72 by surgery first, and 7 by stenting after surgery. The mean age; initial NIHSS score; time from symptom onset to treatment; post-treatment clinical improvement; post-treatment clinical worsening; mortality rate; follow-up duration; and follow-up mRS were, respectively, for each group (local cohort, CS before AS, ACS, CS after AS): 71.2 ± 5.3 yo, 65.5 ± 11.0 yo; 65.3 ± 13.1 yo, 68.7 ± 5.8 yo; 4 ± 8.4, 11.3 ± 8.5, 14.3 ± 8.0, 0; 11.8 ± 14.3 h, 21 ± 39.3 h, 13.6 ± 17.8 h, 13 ± 17.2 h; 56%, 71%, 86%, 57%; 11%, 28%, 0%, 14%; 25%, 12.3%, 14%, 33%; 5.25 ± 2.9 months, 54 months, 6.8 ± 3.8 months, 14 ± 14.4 months; 1 ± 1; 0.25 ± 0.5, 1.3 ± 0.8, 0.68 ± 0.6. Preoperative carotid stenting for hemodynamic cerebral malperfusion by true lumen compression appears to be feasible, and could be effective and safe, although there is still a lack of evidence due to the absence of comparative statistical analysis. The literature, albeit growing, is still limited, and prospective comparative studies are needed. [ABSTRACT FROM AUTHOR]

Objective: To test and evaluate a sofware dedicated to the follow-up of oncological CT scans for potential use in the Radiology department., Materials and Methods: In this retrospective study, 37 oncological patients with baseline and follow-up CT scans were reinterpreted using a dedicated software. Baseline CT scans were chosen from the imaging reports available in our PACS (picture archiving and communicatin systems). Follow-up interpretations were independently assessed with the software. We evaluated the target lesion sums and the tumor response based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors)., Results: There was no significant difference in the target lesion sums and the tumor response assessments between the PACS data and the imaging software. There was no over or underestimation of the disease with the software. There was a sigificant deviation (progression versus stability) in three cases. For two patients, this difference was related to the evaluation of the response of non-target lesions. The difference in the third patient was due to comparison with a previous CT scan than to the baseline exam. There was a miscalculation in 13 % of the reports and in 28 % of the cases the examination was compared to the previous CT scan. Finally, the tumor response was not detailed in 43 % of the follow-up reports., Conclusion: The use of dedicated oncology monitoring software could help in reducing intepretation time and in limiting human errors., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Masson SAS on behalf of Société française de radiologie.)

Purpose: To develop and evaluate a complete deep learning pipeline that allows fully automated end-diastolic left ventricle (LV) cardiac MRI segmentation, including trabeculations and automatic quality control of the predicted segmentation., Materials and Methods: This multicenter retrospective study includes training, validation, and testing datasets of 272, 27, and 150 cardiac MR images, respectively, collected between 2012 and 2018. The reference standard was the manual segmentation of four LV anatomic structures performed on end-diastolic short-axis cine cardiac MRI: LV trabeculations, LV myocardium, LV papillary muscles, and the LV blood cavity. The automatic pipeline was composed of five steps with a DenseNet architecture. Intraobserver agreement, interobserver agreement, and interaction time were recorded. The analysis includes the correlation between the manual and automated segmentation, a reproducibility comparison, and Bland-Altman plots., Results: The automated method achieved mean Dice coefficients of 0.96 ± 0.01 (standard deviation) for LV blood cavity, 0.89 ± 0.03 for LV myocardium, and 0.62 ± 0.08 for LV trabeculation (mean absolute error, 3.63 g ± 3.4). Automatic quantification of LV end-diastolic volume, LV myocardium mass, LV trabeculation, and trabeculation mass-to-total myocardial mass (TMM) ratio showed a significant correlation with the manual measures ( r = 0.99, 0.99, 0.90, and 0.83, respectively; all P < .01). On a subset of 48 patients, the mean Dice value for LV trabeculation was 0.63 ± 0.10 or higher compared with the human interobserver (0.44 ± 0.09; P < .01) and intraobserver measures (0.58 ± 0.09; P < .01). Automatic quantification of the trabeculation mass-to-TMM ratio had a higher correlation (0.92) compared with the intra- and interobserver measures (0.74 and 0.39, respectively; both P < .01)., Conclusion: Automated deep learning framework can achieve reproducible and quality-controlled segmentation of cardiac trabeculations, outperforming inter- and intraobserver analyses. Supplemental material is available for this article. © RSNA, 2020., Competing Interests: Disclosures of Conflicts of Interest: : A.B. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: institution has patents: SATT-SUD-EST, date of deposit 12/13/2019, demand number: DSO000042312, record number: DSO2019018721, depositors: AMU/CNRS/AP-HM. J.F. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: institution has patents: SATT-SUD-EST, patent deposit number: DSO2019018721. Z.B. disclosed no relevant relationships. G.H. disclosed no relevant relationships. A.L. disclosed no relevant relationships. M.B. disclosed no relevant relationships. L.B. disclosed no relevant relationships. F.P. disclosed no relevant relationships. J.N.D. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: author is consultant for Microport; author paid for lectures by Bayer Healthcare, Siemens, Shire Takeda, and MSD; author received travel accommodations from GE Healthcare. Other relationships: disclosed no relevant relationships. B.G. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: institution (Aix Marseille University) has issued patents: A software is protected by the Satt Sud Est, deposit number DSO2019018721. A.J. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed no relevant relationships. Other relationships: institution has issued patents: CNRS/AMU/APHM, date of deposit 12/13/2019, demand number: DSO000042312, record number: DSO2019018721, depositors: AMU/CNRS/AP-HM., (2021 by the Radiological Society of North America, Inc.)