While immune checkpoint inhibitors are disrupting the management of patients with cancer, anecdotal occurrences of rapid progression (i.e., hyperprogressive disease or HPD) under these agents have ...been described, suggesting potentially deleterious effects of these drugs. The prevalence, the natural history, and the predictive factors of HPD in patients with cancer treated by anti-PD-1/PD-L1 remain unknown.
Medical records from all patients (
= 218) prospectively treated in Gustave Roussy by anti-PD-1/PD-L1 within phase I clinical trials were analyzed. The tumor growth rate (TGR) prior ("REFERENCE"; REF) and upon ("EXPERIMENTAL"; EXP) anti-PD-1/PD-L1 therapy was compared to identify patients with accelerated tumor growth. Associations between TGR, clinicopathologic characteristics, and overall survival (OS) were computed.
HPD was defined as a RECIST progression at the first evaluation and as a ≥2-fold increase of the TGR between the REF and the EXP periods. Of 131 evaluable patients, 12 patients (9%) were considered as having HPD. HPD was not associated with higher tumor burden at baseline, nor with any specific tumor type. At progression, patients with HPD had a lower rate of new lesions than patients with disease progression without HPD (
< 0.05). HPD is associated with a higher age (
< 0.05) and a worse outcome (overall survival). Interestingly, REF TGR (before treatment) was inversely correlated with response to anti-PD-1/PD-L1 (
< 0.05) therapy.
A novel aggressive pattern of hyperprogression exists in a fraction of patients treated with anti-PD-1/PD-L1. This observation raises some concerns about treating elderly patients (>65 years old) with anti-PD-1/PD-L1 monotherapy and suggests further study of this phenomenon.
.
In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. However, evaluation of tumor response to ...anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited because morphological change in tumor may occur earlier than tumor size change. Here we present an analysis utilizing a deep learning (DL) network to characterize tumor morphological change for response assessment in mCRC patients. We retrospectively analyzed 1,028 mCRC patients who were prospectively included in the VELOUR trial (NCT00561470). We found that DL network was able to predict early on-treatment response in mCRC and showed better performance than its size-based counterpart with C-Index: 0.649 (95% CI: 0.619,0.679) vs. 0.627 (95% CI: 0.567,0.638), p = 0.009, z-test. The integration of DL network with size-based methodology could further improve the prediction performance to C-Index: 0.694 (95% CI: 0.661,0.720), which was superior to size/DL-based-only models (all p < 0.001, z-test). Our study suggests that DL network could provide a noninvasive mean for quantitative and comprehensive characterization of tumor morphological change, which may potentially benefit personalized early on-treatment decision making.
Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, ...docetaxel, and gefitinib.
Data were collected prospectively and analyzed retrospectively across multicenter clinical trials nivolumab,
= 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel,
= 50, CheckMate017; gefitinib,
= 46, (NCT00588445). Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity.
The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival.
Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.
Purpose
We aimed to evaluate if imaging biomarkers on FDG PET are associated with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC) treated with immune checkpoint ...inhibitors (ICIs).
Methods
In this retrospective monocentric study, we included 109 patients with advanced NSCLC who underwent baseline FDG PET/CT before ICI initiation between July 2013 and September 2018. Clinical, biological (including dNLR = neutrophils/leukocytes minus neutrophils), pathological and PET parameters (tumor SUVmax, total metabolic tumor volume TMTV) were evaluated. A multivariate prediction model was developed using Cox models for progression-free survival (PFS) and overall survival (OS). The association between biomarkers on FDG PET/CT and disease clinical benefit (DCB) was tested using logistic regression.
Results
Eighty patients were eligible. Median follow-up was 11.6 months (95%CI 7.7–15.5). Sixty-four and 52 patients experienced progression and death, respectively. DCB was 40%. In multivariate analyses, TMTV > 75 cm
3
and dNLR > 3 were associated with shorter OS (HR 2.5, 95%CI 1.3–4.7 and HR 3.3, 95%CI 1.6–6.4) and absence of DCB (OR 0.3, 95%CI 0.1–0.9 and OR 0.4, 95%CI 0.2–0.9). Unlike TMTV, dNLR was a significant prognostic factor for PFS (HR 1.9, 95%CI 1.1–3.3) along with anemia (HR 1.9, 95%CI 1.2–3.8). No association was observed between tumor SUVmax and PFS or OS.
Conclusion
Baseline tumor burden (TMTV) on FDG PET/CT scans and inflammatory status (dNLR) were associated with poor OS and absence of DCB for ICI treatment in advanced NSCLC patients, unlike tumor SUVmax, and may be used together to improve the selection of appropriate candidates.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
We evaluated whether the optimal selection of CT reconstruction settings enables the construction of a radiomics model to predict epidermal growth factor receptor (EGFR) mutation status in primary ...lung adenocarcinoma (LAC) using standard of care CT images. Fifty-one patients (EGFR:wildtype = 23:28) with LACs of clinical stage I/II/IIIA were included in the analysis. The LACs were segmented in four conditions, two slice thicknesses (Thin: 1 mm; Thick: 5 mm) and two convolution kernels (Sharp: B70f/B70s; Smooth: B30f/B31f/B31s), which constituted four groups: (1) Thin-Sharp, (2) Thin-Smooth, (3) Thick-Sharp, and (4) Thick-Smooth. Machine learning algorithms selected and combined 1,695 quantitative image features to build prediction models. The performance of prediction models was assessed by calculating the area under the curve (AUC). The best prediction model yielded AUC (95%CI) = 0.83 (0.68, 0.92) using the Thin-Smooth reconstruction setting. The AUC of models using thick slices was significantly lower than that of thin slices (P < 10
), whereas the impact of reconstruction kernel was not significant. Our study showed that the optimal prediction of EGFR mutational status in early stage LACs was achieved by using thin CT-scan slices, independently of convolution kernels. Results from the prediction model suggest that tumor heterogeneity is associated with EGFR mutation.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Purpose
To evaluate the impact of blended learning using a combination of educational resources (flipped classroom and short videos) on medical students’ (MSs) for radiology learning.
Material and ...methods
A cohort of 353 MSs from 2015 to 2018 was prospectively evaluated. MSs were assigned to four groups (high, high-intermediate, low-intermediate, and low achievers) based on their results to a 20-MCQs performance evaluation referred to as the pretest. MSs had then free access to a self-paced course totalizing 61 videos based on abdominal imaging over a period of 3 months. Performance was evaluated using the change between posttest (the same 20 MCQs as pretest) and pretest results. Satisfaction was measured using a satisfaction survey with directed and spontaneous feedbacks. Engagement was graded according to audience retention and attendance on a web content management system.
Results
Performance change between pre and posttest was significantly different between the four categories (ANOVA,
P
= 10
−9
): low pretest achievers demonstrated the highest improvement (mean ± SD, + 11.3 ± 22.8 points) while high pretest achievers showed a decrease in their posttest score (mean ± SD, − 3.6 ± 19 points). Directed feedback collected from 73.3% of participants showed a 99% of overall satisfaction. Spontaneous feedback showed that the concept of “pleasure in learning” was the most cited advantage, followed by “flexibility
.
” Engagement increased over years and the number of views increased of 2.47-fold in 2 years.
Conclusion
Learning formats including new pedagogical concepts as blended learning, and current technologies allow improvement in medical student’s performance, satisfaction, and engagement.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Purpose
To compare the prognostic value of imaging biomarkers derived from a quantitative analysis of baseline 18F-FDG-PET/CT in patients with mucosal melanoma (Muc-M) or cutaneous melanoma (Cut-M) ...treated with immune checkpoint inhibitors (ICIs).
Methods
In this retrospective monocentric study, we included 56 patients with non-resectable Muc-M (
n
= 24) or Cut-M (
n
= 32) who underwent baseline 18F-FDG-PET/CT before treatment with ICIs between 2011 and 2017. Parameters were extracted from (i) tumoral tissues: SUVmax, SUVmean, TMTV (total metabolic tumor volume), and TLG (total lesion glycolysis) and (ii) lymphoid tissues: BLR (bone marrow-to-liver SUVmax ratio) and SLR (spleen-to-liver SUVmax ratio). Association with survival and response was evaluated using Cox prediction models, Student’s
t
tests, and Spearman’s correlation respectively.
p
< 0.05 was considered significant.
Results
Majority of ICIs were anti-PD1 (92.9%,
n
= 52/56). All 18F-FDG-PET/CT were positive. Overall (Muc-M to Cut-M), ORR was 33%:42%, DCR was 56%:69%, median follow-up was 25.0:28.9 months, median PFS was 4.7:10.7 months, and median OS was 23.9:28.3 months. In Muc-M, increased tumor SUVmax was associated with shorter OS while it was not correlated with PFS, ORR, or DCR. In Cut-M, increased TMTV and increased BLR were independently associated with shorter OS, shorter PFS, and lower response (ORR, DCR).
Conclusion
While all Muc-M and Cut-M were FDG avid, prognostic imaging biomarkers differed. For Muc-M patients treated with ICI, the only prognostic imaging biomarker was a high baseline maximal glycolytic activity (SUVmax), whereas for Cut-M patients, baseline metabolic tumor burden or bone marrow metabolism was negatively correlated to ICI response duration.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient ...prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman's Rho ranged between 0.41 and 0.59 (P = 0.0001, Bonferroni correction). Multivariate linear analysis revealed that entropy in SM#1 is significantly associated with (i) primary tumor type; (ii) entropy in SM#2 (same malignant process); (iii) ROI area size; (iv) metastasis site; and (v) entropy in the psoas muscle (reference tissue). Entropy was a logarithmic function of ROI area in normal control tissues (aorta, psoas) and in mathematical models (P < 0.01). We concluded that entropy is a tumor-specific metric only if confounding factors are corrected.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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