Evaluation of radio-immunotherapy sequence on immunological responses and clinical outcomes in patients with melanoma brain metastases (ELEKTRA)

In patients with melanoma brain metastases (MBM), a combination of radiotherapy (RT) with immune checkpoint inhibitors (ICI) is routinely used. However, the best sequence of radio-immunotherapy (RIT) remains unclear. In an exploratory phase 2 trial, MBM patients received RT (stereotactic or whole-brain radiotherapy depending on the number of MBM) combined with ipilimumab (ipi) ± nivolumab (nivo) in different sequencing (Rad-ICI or ICI-Rad). Comparators arms included patients treated with ipi-free systemic treatment or without RT (in MBM-free patients). The primary endpoints were radiological and immunological responses in the peripheral blood. Secondary endpoints were progression-free survival (PFS) and overall survival (OS). Of 106 screened, 92 patients were included in the study. Multivariate analysis revealed an advantage for patients starting with RT (Rad-ICI) for overall response rate (RR: p = .007; HR: 7.88 (95%CI: 1.76-35.27)) and disease control rate (DCR: p = .036; HR: 6.26 (95%CI: 1.13-34.71)) with a trend for a better PFS (p = .162; HR: 1.64 (95%CI: 0.8-3.3)). After RT plus two cycles of ipi-based ICI in both RIT sequences, increased frequencies of activated CD4, CD8 T cells and an increase in melanoma-specific T cell responses were observed in the peripheral blood. Lasso regression analysis revealed a significant clinical benefit for patients treated with Rad-ICI sequence and immunological features, including high frequencies of memory T cells and activated CD8 T cells in the blood. This study supports increasing evidence that sequencing RT followed by ICI treatment may have better effects on the immunological responses and clinical outcomes in MBM patients

Immunotherapies for the Treatment of Uveal Melanoma—History and Future

Background: Uveal melanoma is the most common primary intraocular malignancy among adults. It is, nevertheless, a rare disease, with an incidence of approximately one case per 100,000 individuals per year in Europe. Approximately half of tumors will eventually metastasize, and the liver is the organ usually affected. No standard-of-care treatment exists for metastasized uveal melanoma. Chemotherapies or liver-directed treatments do not usually result in long-term tumor control. Immunotherapies are currently the most promising therapy option available. Methods: We reviewed both relevant recent literature on PubMed concerning the treatment of uveal melanoma with immunotherapies, and currently investigated drugs on ClinicalTrials.gov. Our own experiences with immune checkpoint blockers are included in a case series of 20 patients. Results: Because few clinical trials have been conducted for metastasized uveal melanoma, no definitive treatment strategy exists for this rare disease. The outcomes of most immunotherapies are poor, especially compared with cutaneous melanoma. However, encouraging results have been found for some very recently investigated agents such as the bispecific tebentafusp, for which a remarkably increased one-year overall survival rate, and similarly increased disease control rate, were observed in early phase studies. Conclusions: The treatment of metastatic uveal melanoma remains challenging, and almost all patients still die from the disease. Long-term responses might be achievable by means of new immunological strategies. Patients should therefore be referred to large medical centers where they can take part in controlled clinical studies

Multiarm study comparing patient-reported and clinical outcome measures in patients undergoing antipsoriatic therapy with non-biological systemic agents in a real-world setting

Background Although the inclusion of patients’ preferences and needs is essential for therapy adherence, the assessment of patient-reported outcome measures in clinical trials is often neglected. Therefore, the aim of this study was to quantify several patient-reported outcome measures in psoriasis patients undergoing systemic therapy in a real-life clinical setting. Methods This clinical trial has been designed as a prospective, multiarm study to investigate the treatment satisfaction, adherence to therapy, quality of life (QoL), and clinical response in a real-life clinical setting during the initial 6 months of treatment with apremilast, methotrexate, and fumaric acids in 80 patients suffering from plaque psoriasis. Results The treatment satisfaction for the three systemic therapies was rated ‘sufficient’ with a mean (±SD) Treatment Satisfaction Questionnaire for Medication (TSQM) score of 275.0 (±62.7). Most potential for improvement was seen in the ‘effectiveness’ domain (54.3 ± 21.5). The highest treatment satisfaction level in all four domains (convenience, effectiveness, global satisfaction, and side-effects) was seen in the methotrexate group with a mean TSQM score of 306.3 ± 50.9, followed by apremilast (267.1 ± 61.6) and fumaric acids (254.9 ± 65.0; p = 0.005). Analysis of the TSQM revealed a considerable discrepancy between patient-reported clinical response and the actual Psoriasis Area and Severity Index (PASI) reduction. This applies equally to the patient- vs. physician-reported side-effects. Conclusions This real-life study demonstrates that an adequate assessment of antipsoriatic drugs by PASI-reduction alone is not sufficient and underlines the importance of patient-reported outcome measures not only in clinical trials, but also for improved patient care

Complete Metabolic Response in FDG-PET-CT Scan before Discontinuation of Immune Checkpoint Inhibitors Correlates with Long Progression-Free Survival

Checkpoint inhibitors have revolutionized the treatment of patients with metastasized melanoma. However, it remains unclear when to stop treatment. We retrospectively analyzed 45 patients (median age 64 years; 58% male) with metastasized melanoma from 3 cancer centers that received checkpoint inhibitors and discontinued therapy due to either immune-related adverse events or patient decision after an (18F)2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) combined with a low-dose CT scan (FDG-PET-CT) scan without signs for disease progression. After a median of 21 (range 1–42) months of immunotherapy an FDG-PET-CT scan was performed to evaluate disease activity. In these, 32 patients (71%) showed a complete metabolic response (CMR) and 13 were classified as non-CMR. After a median follow-up of 34 (range 1–70) months, 3/32 (9%) of CMR patients and 6/13 (46%) of non-CMR patients had progressed (p = 0.007). Progression-free survival (PFS), as estimated from the date of last drug administration, was significantly longer among CMR patients than non-CMR (log-rank: p = 0.001; hazard ratio: 0.127; 95% CI: 0.032–0.511). Two-year PFS was 94% among CMR patients and 62% among non-CMR patients. Univariable Cox regression showed that metabolic response was the only parameter which predicted PFS (p = 0.004). Multivariate analysis revealed that metabolic response predicted disease progression (p = 0.008). In conclusion, our findings suggest that patients with CMR in an FDG-PET-CT scan may have a favorable outcome even if checkpoint inhibition is discontinued

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

Background: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. Methods: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. Interpretation: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity. (C) 2019 The Author(s). Published by Elsevier Ltd