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.
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Radiomics relies on the extraction of a wide variety of quantitative image-based features to provide decision support. Magnetic resonance imaging (MRI) contributes to the personalization of patient ...care but suffers from being highly dependent on acquisition and reconstruction parameters. Today, there are no guidelines regarding the optimal pre-processing of MR images in the context of radiomics, which is crucial for the generalization of published image-based signatures. This study aims to assess the impact of three different intensity normalization methods (Nyul, WhiteStripe, Z-Score) typically used in MRI together with two methods for intensity discretization (fixed bin size and fixed bin number). The impact of these methods was evaluated on first- and second-order radiomics features extracted from brain MRI, establishing a unified methodology for future radiomics studies. Two independent MRI datasets were used. The first one (DATASET1) included 20 institutional patients with WHO grade II and III gliomas who underwent post-contrast 3D axial T1-weighted (T1w-gd) and axial T2-weighted fluid attenuation inversion recovery (T2w-flair) sequences on two different MR devices (1.5 T and 3.0 T) with a 1-month delay. Jensen-Shannon divergence was used to compare pairs of intensity histograms before and after normalization. The stability of first-order and second-order features across the two acquisitions was analysed using the concordance correlation coefficient and the intra-class correlation coefficient. The second dataset (DATASET2) was extracted from the public TCIA database and included 108 patients with WHO grade II and III gliomas and 135 patients with WHO grade IV glioblastomas. The impact of normalization and discretization methods was evaluated based on a tumour grade classification task (balanced accuracy measurement) using five well-established machine learning algorithms. Intensity normalization highly improved the robustness of first-order features and the performances of subsequent classification models. For the T1w-gd sequence, the mean balanced accuracy for tumour grade classification was increased from 0.67 (95% CI 0.61-0.73) to 0.82 (95% CI 0.79-0.84, P = .006), 0.79 (95% CI 0.76-0.82, P = .021) and 0.82 (95% CI 0.80-0.85, P = .005), respectively, using the Nyul, WhiteStripe and Z-Score normalization methods compared to no normalization. The relative discretization makes unnecessary the use of intensity normalization for the second-order radiomics features. Even if the bin number for the discretization had a small impact on classification performances, a good compromise was obtained using the 32 bins considering both T1w-gd and T2w-flair sequences. No significant improvements in classification performances were observed using feature selection. A standardized pre-processing pipeline is proposed for the use of radiomics in MRI of brain tumours. For models based on first- and second-order features, we recommend normalizing images with the Z-Score method and adopting an absolute discretization approach. For second-order feature-based signatures, relative discretization can be used without prior normalization. In both cases, 32 bins for discretization are recommended. This study may pave the way for the multicentric development and validation of MR-based radiomics biomarkers.
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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, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
High-throughput genomic analyses may improve outcomes in patients with advanced cancers. MOSCATO 01 is a prospective clinical trial evaluating the clinical benefit of this approach. Nucleic acids ...were extracted from fresh-frozen tumor biopsies and analyzed by array comparative genomic hybridization, next-generation sequencing, and RNA sequencing. The primary objective was to evaluate clinical benefit as measured by the percentage of patients presenting progression-free survival (PFS) on matched therapy (PFS2) 1.3-fold longer than the PFS on prior therapy (PFS1). A total of 1,035 adult patients were included, and a biopsy was performed in 948. An actionable molecular alteration was identified in 411 of 843 patients with a molecular portrait. A total of 199 patients were treated with a targeted therapy matched to a genomic alteration. The PFS2/PFS1 ratio was >1.3 in 33% of the patients (63/193). Objective responses were observed in 22 of 194 patients (11%; 95% CI, 7%-17%), and median overall survival was 11.9 months (95% CI, 9.5-14.3 months).
This study suggests that high-throughput genomics could improve outcomes in a subset of patients with hard-to-treat cancers. Although these results are encouraging, only 7% of the successfully screened patients benefited from this approach. Randomized trials are needed to validate this hypothesis and to quantify the magnitude of benefit. Expanding drug access could increase the percentage of patients who benefit.
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In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics ...results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.
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The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan ...data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.
Anti-PD-(L)1 can provide overall survival (OS) benefits over conventional treatments for patients with many different cancer types. However, the long-term outcome of cancer patients responding to ...these therapies remains unknown. This study is an exploratory study that aimed to describe the long-term survival of patients responding to anti-PD-(L)1 monotherapy across multiple cancer types.
Data from patients treated with an anti-PD-(L)1 monotherapy in a phase I trial at Gustave Roussy were retrospectively analyzed over a period of 5 years. All cancer types (
= 19) were included. Clinical and biological factors associated with response, long-term survival, and secondary refractory disease were studied.
Among 262 eligible patients, the overall objective response rate was 29%. The median progression-free survival of responder patients (RP) at 3 months was 30 months, and the median OS of RP was not reached after a median follow-up of 34 months. In RPs, 3- and 5-year OS percentages were 84% and 64%, respectively. No death occurred in the 21 complete responders (CR) during the overall follow-up. However, many partial responders (PR) showed subsequent tumor relapses to treatment. Long responders (response ≥2 years) represented 11.8% of the overall population. These findings should be validated in further prospective studies.
There are currently no differences in therapeutic strategies between CRs and PRs to anti-PD-(L)1. We found a striking difference in OS between these two types of responses. Our results are in favor of evaluating patient stratification strategies and intensification of treatments when tumor lesions of a partial responder to immunotherapy stop improving.
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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, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
Response Evaluation Criteria in Solid Tumors (RECIST) evaluation does not take into account the pretreatment tumor kinetics and may provide incomplete information about experimental drug activity. ...Tumor growth rate (TGR) allows for a dynamic and quantitative assessment of the tumor kinetics. How TGR varies along the introduction of experimental therapeutics and is associated with outcome in phase I patients remains unknown.
Medical records from all patients (N = 253) prospectively treated in 20 phase I trials were analyzed. TGR was computed during the pretreatment period (reference) and the experimental period. Associations between TGR, standard prognostic scores Royal Marsden Hospital (RMH) score, and outcome progression-free survival (PFS) and overall survival (OS) were computed (multivariate analysis).
We observed a reduction of TGR between the reference versus experimental periods (38% vs. 4.4%; P < 0.00001). Although most patients were classified as stable disease (65%) or progressive disease (25%) by RECIST at the first evaluation, 82% and 65% of them exhibited a decrease in TGR, respectively. In a multivariate analysis, only the decrease of TGR was associated with PFS (P = 0.004), whereas the RMH score was the only variable associated with OS (P = 0.0008). Only the investigated regimens delivered were associated with a decrease of TGR (P < 0.00001, multivariate analysis). Computing TGR profiles across different clinical trials reveals specific patterns of antitumor activity.
Exploring TGR in phase I patients is simple and provides clinically relevant information: (i) an early and subtle assessment of signs of antitumor activity; (ii) independent association with PFS; and (iii) it reveals drug-specific profiles, suggesting potential utility for guiding the further development of the investigational drugs.
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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