Health-related quality of life in patients with advanced melanoma treated with ipilimumab: prognostic implications and changes during treatment

Background: We have previously reported that the safety and efficacy of ipilimumab in real-world patients with metastatic melanoma were comparable to clinical trials. Few studies have explored health-related quality of life (HRQL) in real-world populations receiving checkpoint inhibitors. This study reports HRQL in real-world patients receiving ipilimumab and assesses the prognostic value of patient-reported outcome measures. Patients and methods: Ipi4 (NCT02068196) was a prospective, multicentre, interventional phase IV trial. Real-world patients (N = 151) with metastatic melanoma were treated with ipilimumab 3 mg/kg intravenously as labelled. HRQL was assessed by the European Organisation of Research and Treatment of Cancer Quality of Life Questionnaire at baseline and after 10-12 weeks. Results: The European Organisation of Research and Treatment of Cancer Quality of Life Questionnaire was completed by 93% (141/151 patients) at baseline, and by 82% at 10-12 weeks. Poor performance status and elevated C-reactive protein (CRP) were associated with worse baseline HRQL. Clinically relevant and statistically significant deteriorations in HRQL from baseline to weeks 10-12 were reported (P <0.05). Baseline physical functioning [hazard ratio (HR) 1.96, P = 0.016], role functioning (HR 2.15, P <0.001), fatigue (HR 1.60, P = 0.030), and appetite loss (HR 1.76, P = 0.012) were associated with poorer overall survival independent of performance status, lactate dehydrogenase (LDH), and CRP. We further developed a prognostic model, combining HRQL outcomes with performance status, LDH, and CRP. This model identified three groups with large and statistically significant differences in survival. Conclusions: Systemic inflammation is associated with impaired HRQL. During treatment with ipilimumab, HRQL deteriorated significantly. Combining HRQL outcomes with objective risk factors provided additional prognostic information that may aid clinical decision making.publishedVersio

Ipilimumab in a real-world population: A prospective Phase IV trial with long-term follow-up

Ipilimumab was the first treatment that improved survival in advanced melanoma. Efficacy and toxicity in a real-world setting may differ from clinical trials, due to more liberal eligibility criteria and less intensive monitoring. Moreover, high costs and lack of biomarkers have raised cost-benefit concerns about ipilimumab in national healthcare systems and limited its use. Here, we report the prospective, interventional study, Ipi4 (NCT02068196), which aimed to investigate the toxicity and efficacy of ipilimumab in a real-world population with advanced melanoma. This national, multicentre, phase IV trial included 151 patients. Patients received ipilimumab 3 mg/kg intravenously and were followed for at least 5 years or until death. Treatment interruption or cessation occurred in 38%, most frequently due to disease progression (19%). Treatment-associated grade 3 to 4 toxicity was observed in 28% of patients, and immune-related toxicity in 56%. The overall response rate was 9%. Median overall survival was 12.1 months (95% CI: 8.3-15.9); and progression-free survival 2.7 months (95% CI: 2.6-2.8). After 5 years, 20% of patients were alive. In a landmark analysis from 6 months, improved survival was associated with objective response (HR 0.16, P = .001) and stable disease (HR 0.49, P = .005) compared to progressive disease. Poor performance status, elevated lactate dehydrogenase and C-reactive protein were identified as biomarkers. This prospective trial represents the longest reported follow-up of a real-world melanoma population treated with ipilimumab. Results indicate safety and efficacy comparable to phase III trials and suggest that the use of ipilimumab can be based on current cost-benefit estimates.publishedVersio

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data

IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.MethodsWe adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.ResultsThe 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively.Discussion/ConclusionOur results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2

Melanoma staging: Varying precision and terminal digit clustering in Breslow thickness data is evident in a population-based study

Background Errors in Breslow thickness reporting can give misclassification of T category, an important classifier in melanoma staging. Objective We sought to investigate precision (number of digits) and terminal digit clustering in Breslow thickness and potential consequences for T category. Methods All first primary and morphologically verified invasive melanomas in Norway between 2008 and 2015 were included. A smoothing model was fitted to estimate the underlying Breslow thickness distribution without digit clustering. Results Thickness was reported for 13,057 (97.5%) patients; the median was 1.0 mm (range, 0.09-85). It was reported as whole numbers (15.6%), to 1 decimal (78.2%) and 2 decimal places (6.2%)—thin tumors with more precision than thick tumors. Terminal digit clustering was found with marked peaks in the observed frequency distribution for terminal digits 0 and 5, and with drops around these peaks. Terminal digit clustering increased proportions of patients classified with T1 and T4 tumors and decreased proportions classified with T2 and T3. Limitations Breslow thickness was not reported in 2.5% of cases. Conclusions The Norwegian recommendation of measurement to the nearest 0.1 mm was not followed. Terminal digit clustering was marked, with consequences for T category. Pathologists, clinicians, and epidemiologists should know that clustering of thickness data around T category cut points can impact melanoma staging with consequent effect on patient management and prognosis

Soluble AXL as a marker of disease progression and survival in melanoma.

Receptor tyrosine kinase AXL is a one-pass transmembrane protein upregulated in cancers and associated with lower survival and therapy resistance. AXL can be cleaved by the A Disintegrin and Metalloproteinases (ADAM)10 and ADAM17, yielding a soluble version of the protein. Elevated soluble AXL (sAXL) has been reported to be associated with disease progression in hepatocellular carcinoma, renal cancer, neurofibromatosis type 1 and inflammatory diseases. In the present work, we analyzed sAXL levels in blood from melanoma patients and showed that sAXL increases with disease progression. Additionally, increased sAXL levels were found correlated with shorter two-year survival in stage IV patients treated with ipilimumab. Furthermore, we showed that sAXL levels were related to the percentage of cells expressing AXL in resected melanoma lymph node metastases. This finding was verified in vitro, where sAXL levels in the cell media corresponded to AXL expression in the cells. AXL inhibition using the small-molecular inhibitor BGB324 reduced sAXL levels, while the cellular expression was elevated through increased protein stability. Our findings signify that quantification of sAXL blood levels is a simple and easily assessable method to determine cellular AXL levels and should be further evaluated for its use as a biomarker of disease progression and treatment response

Responses in the diffusivity and vascular function of the irradiated normal brain are seen up until 18 months following SRS of brain metastases

Background MRI may provide insights into longitudinal responses in the diffusivity and vascular function of the irradiated normal-appearing brain following stereotactic radiosurgery (SRS) of brain metastases. Methods Forty patients with brain metastases from non-small cell lung cancer (N = 26) and malignant melanoma (N = 14) received SRS (15–25 Gy). Longitudinal MRI was performed pre-SRS and at 3, 6, 9, 12, and 18 months post-SRS. Measures of tissue diffusivity and vascularity were assessed by diffusion-weighted and perfusion MRI, respectively. All maps were normalized to white matter receiving less than 1 Gy. Longitudinal responses were assessed in normal-appearing brain, excluding tumor and edema, in the LowDose (1–10 Gy) and HighDose (>10 Gy) regions. The Eastern Cooperative Oncology Group (ECOG) performance status was recorded pre-SRS. Results Following SRS, the diffusivity in the LowDose region increased continuously for 1 year (105.1% ± 6.2%; P < .001), before reversing toward pre-SRS levels at 18 months. Transient reductions in microvascular cerebral blood volume (P < .05), blood flow (P < .05), and vessel densities (P < .05) were observed in LowDose at 6–9 months post-SRS. Correspondingly, vessel calibers in LowDose transiently increased at 3–9 months (P < .01). The responses in HighDose displayed similar trends as in LowDose, but with larger interpatient variations. Vascular responses followed pre-SRS ECOG status. Conclusions Our results imply that even low doses of radiation to normal-appearing brain following cerebral SRS induce increased diffusivity and reduced vascular function for up until 18 months. In particular, the vascular responses indicate the reduced ability of the normal-appearing brain tissue to form new capillaries. Assessing the potential long-term neurologic effects of SRS on the normal-appearing brain is warranted

Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

Abstract The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p < 0.001) compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences

Noise dependency in vascular parameters from combined gradient-echo and spin-echo DSC MRI

Dynamic susceptibility contrast (DSC) imaging is a widely used technique for assessment of cerebral blood volume (CBV). With combined gradient-echo and spin-echo DSC techniques, measures of the underlying vessel size and vessel architecture can be obtained from the vessel size index (VSI) and vortex area, respectively. However, how noise, and specifically the contrast-to-noise ratio (CNR), affect the estimations of these parameters has largely been overlooked. In order to address this issue, we have performed simulations to generate DSC signals with varying levels of CNR, defined by the peak of relaxation rate curve divided by the standard deviation of the baseline. Moreover, DSC data from 59 brain cancer patients were acquired at two different 3 T-scanners (N = 29 and N = 30, respectively), where CNR and relative parameter maps were obtained. Our simulations showed that the measured parameters were affected by CNR in different ways, where low CNR led to overestimations of CBV and underestimations of VSI and vortex area. In addition, a higher noise-sensitivity was found in vortex area than in CBV and VSI. Results from clinical data were consistent with simulations, and indicated that CNR < 4 gives highly unreliable measurements. Moreover, we have shown that the distribution of values in the tumour regions could change considerably when voxels with CNR below a given cut off are excluded when generating the relative parameter maps. The widespread use of CBV and attractive potential of VSI and vortex area, makes the noise-sensitivity of these parameters found in our study relevant for further use and development of the DSC imaging technique. Our results suggest that the CNR has considerable impact on the measured parameters, with the potential to affect the clinical interpretation of DSC-MRI, and should therefore be taken into account in the clinical decision-making process