High PDL1/PDL2 gene expression correlates with worse outcome in primary mediastinal large B-cell lymphoma.

Primary mediastinal B-cell lymphoma (PMBL) is an uncommon entity of aggressive B-cell lymphoma with an unusually good prognosis, except for 10-15% of chemotherapy-refractory cases. To identify earlier these higher risk patients, we performed molecular characterization of a retrospective multicenter cohort of patients treated with firstline immunochemotherapy. The traits of the patients with gene-expression profiling data (n = 120) were as follows: median age of 34 years (range, 18-67 years); female sex, 58.3%; elevated lactate dehydrogenase, 82.5%; Eastern Cooperative Oncology Group performance status score of 0 to 1, 85.7%; Ann Arbor stage I/II, 55%; International Prognostic Index score of 1 to 2, 64.4%; and median metabolic tumor volume, 290.4 cm3 (range, 15.7-1147.5 cm3). Among all 137 markers tested for correlation with survival data, only programmed death-ligand (PDL) 1 and PDL2 expression showed a prognostic impact. Overall, both PDL1 and PDL2 genes were highly expressed in 37 patients (30.8%; PDL1high/PDL2high). The baseline clinical characteristics of patients with PDL1high/PDL2high were similar to those of other patients. In univariate analysis, PDL1high/PDL2high status was associated with poor progression-free survival (PFS) (hazard ratio [HR], 4.292) and overall survival (OS; HR, 8.24). In multivariate analysis, PDL1high/PDL2high status was an independent prognostic factor of adverse outcomes (PFS: HR, 5.22; OS: HR, 10.368). We validated these results in an independent cohort of 40 patients and confirmed the significant association between PDL1high/PDL2high status and inferior PFS (HR, 6.11). High PDL1/PDL2 gene expression defines a population with strong immune privilege and poorer outcomes from standard chemotherapy who might benefit from firstline checkpoint inhibitor therapy.

Synergic prognostic value of 3D CT scan subcutaneous fat and muscle masses for immunotherapy-treated cancer.

BACKGROUND: Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy. METHODS: We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpoint-inhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis. RESULTS: In the overall cohort, 1-year mortality rate was 0.496 (95% CI: 0.457 to 0.537) for 309 events and 5-year mortality rate was 0.196 (95% CI: 0.165 to 0.233) for 477 events. In the univariate Kaplan-Meier analysis, prognosis was worse (p<0.001) for patients with low SFM (<3.95 kg/m2), low FBM (<3.26 kg/m2), low VFM (<0.91 kg/m2), low MBM (<5.85 kg/m2) and low BMI (<24.97 kg/m2). The same parameters were significant in the Cox univariate analysis (p<0.001) and, in the multivariate stepwise Cox analysis, the significant parameters were MBM (p<0.0001), SFM (0.013) and WL (0.0003). In subanalyses according to the type of cancer, all body composition parameters were statistically significant for NSCLC in ROC, KM and Cox univariate analysis while, for melanoma, none of them, except MBM, was statistically significant. In multivariate Cox analysis, the significant parameters for NSCLC were MBM (HR=0.81, p=0.0002), SFM (HR=0.94, p=0.02) and WL (HR=1.06, p=0.004). For NSCLC, a KM analysis combining SFM and MBM was able to separate the population in three categories with the worse prognostic for the patients with both low SFM (<5.22 kg/m2) and MBM (<6.86 kg/m2) (p<0001). On the external validation cohort, combination of low SFM and low MBM was pejorative with 63% of mortality at 1 year versus 25% (p=0.0029). CONCLUSIONS: 3D measured low SFM and MBM are significant prognosis factors of NSCLC treated by immune checkpoint inhibitors and can be combined to improve the prognostic value.

Anti-leucine-rich glioma-inactivated 1 encephalitis revealed by a manic episode: insights from frontal lobe dysfunction in neuropsychiatry through neuropsychology and metabolic imaging. A case report.

Background: Anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis is a limbic encephalitis that rarely presents as an isolated psychiatric syndrome. Case presentation: A 70-year-old patient first presented with behavioral disorder including hyperactivity, euphoria, with disinhibition and accelerated speech associated with severe insomnia and cognitive disorder. A manic episode was diagnosed and he received various psychotropic medications with no improvement. Invesitgations were negative (MRI showed T2 aspecific hyperintensities with no hyperintensities in limbic regions and EEG was normal). He was transferred to a nursing home, with a diagnosis of neurodegenerative condition. Later, he was referred to our unit for further investigations. A cerebral 18F-FDG-PET revealed an association of frontal hypometabolism and temporal and striatum hypermetabolism and CSF analysis revealed slightly increased white blood cell counts. Plasmatic anti-LGI1 antibodies were detected. The patient was treated with intra-venous immunoglobulin (IvIg) but showed no improvement. Second-line treatment (a combination of rituximab and cyclophosmphamide) was then administered for a year, leading to an improvement of neuropsychiatric symptoms and normalization of metabolic impairment on 18F-FDG-PET. Conclusion: In this report, we describe a novel case of a patient withanti-LGI1 encephalitis with a predominant long-term psychiatric presentation. An atypical presentation (such as atypical psychiatric symptoms, neurocognitive disorder, and hyponatremia) should prompt further investigations such as CSF analysis, considering that MRI and EEG may be normal. FDG-PET might be of interest but few data are available in the literature. Early treatment of anti-LGI1 encephalitis is crucial for overall prognosis and may delay the development of dementia in some cases.

High prevalence of pre-existing sarcopenia in critically ill patients with hematologic malignancies admitted to the intensive care unit for sepsis or septic shock.

BACKGROUND & AIMS: We aimed to evaluate body composition (BC) by computed tomography (CT) in hematologic malignancy (HM) patients admitted to the intensive care unit (ICU) for sepsis or septic shock. METHODS: We retrospectively assessed BC and its impact on outcome of 186 patients at the 3rd lumbar (L3) and 12th thoracic vertebral levels (T12) using CT-scan performed before ICU admission. RESULTS: The median patient age was 58.0 [47; 69] years. Patients displayed adverse clinical characteristics at admission with median [q1; q3] SAPS II and SOFA scores of 52 [40; 66] and 8 [5; 12], respectively. The mortality rate in the ICU was 45.7%. Overall survival rates at 1 month after admission in the pre-existing sarcopenic vs. non pre-existing sarcopenic patients were 47.9% (95% CI [37.6; 61.0]) and 55.0% (95% CI [41.6; 72.8]), p = 0.99), respectively, at the L3 level and 48.4% (95% CI [40.4; 58.0]) vs. 66.7% (95% CI [51.1; 87.0]), p = 0.062), respectively, at the T12 level. CONCLUSIONS: Sarcopenia is assessable by CT scan at both the T12 and L3 levels and is highly prevalent in HM patients admitted to the ICU for severe infections. Sarcopenia may contribute to the high mortality rate in the ICU in this population.

Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT.

This paper presents an overview of the third edition of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. The challenge comprises two tasks related to the automatic analysis of FDG-PET/CT images for patients with Head and Neck cancer (H&N), focusing on the oropharynx region. Task 1 is the fully automatic segmentation of H&N primary Gross Tumor Volume (GTVp) and metastatic lymph nodes (GTVn) from FDG-PET/CT images. Task 2 is the fully automatic prediction of Recurrence-Free Survival (RFS) from the same FDG-PET/CT and clinical data. The data were collected from nine centers for a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (DSCagg) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.

Respiratory muscle metabolic activity on PET/CT correlates with obstructive ventilatory defect severity and prognosis in patients undergoing lung cancer surgery.

BACKGROUND AND OBJECTIVE: Respiratory muscle activity is increased in patients with chronic respiratory disease. 18 F-FDG-PET/CT can assess respiratory muscle activity. We hypothesized that respiratory muscles metabolism was correlated to lung function impairment and was associated to prognosis in patients undergoing lung cancer surgery based on the research question whether respiratory muscle metabolism quantitatively correlates with the severity of lung function impairment in patients? Does respiratory muscle hypermetabolism have prognostic value? METHODS: Patients undergoing 18 F-FDG-PET/CT and pulmonary function tests prior to lung cancer surgery were identified. Maximum Standardized Uptake Value (SUVm) were measured in each respiratory muscle group (sternocleidomastoid, scalene, intercostal, diaphragm), normalized against deltoid SUVm. Respiratory muscle hypermetabolism was defined as SUVm >90th centile in any respiratory muscle group. Clinical outcomes were collected from a prospective cohort. RESULTS: One hundred fifty-six patients were included, mostly male [110 (71%)], 53 (34%) with previous diagnosis of COPD. Respiratory muscle SUVm were: scalene: 1.84 [1.51-2.25], sternocleidomastoid 1.64 [1.34-1.95], intercostal 1.01 [0.84-1.16], diaphragm 1.79 [1.41-2.27]. Tracer uptake was inversely correlated to FEV1 for the scalene (r = -0.29, p < 0.001) and SCM (r = -0.17, p = 0.03) respiratory muscle groups and positively correlated to TLC for the scalene (r = 0.17, p = 0.04). Respiratory muscle hypermetabolism was found in 45 patients (28.8%), who had a lower VO2 max (15.4 [14.2-17.5] vs. 17.2 mL/kg/min [15.2-21.1], p = 0.07) and poorer overall survival when adjusting to FEV1% (p < 0.01). CONCLUSION: Our findings show respiratory muscle hypermetabolism is associated with lung function impairment and has prognostic significance. 18 F-FDG/PET-CT should be considered as a tool for assessing respiratory muscle activity and to identify high-risk patients.

Advances in PET and MRI imaging of tumor hypoxia.

Tumor hypoxia is a complex and evolving phenomenon both in time and space. Molecular imaging allows to approach these variations, but the tracers used have their own limitations. PET imaging has the disadvantage of low resolution and must take into account molecular biodistribution, but has the advantage of high targeting accuracy. The relationship between the signal in MRI imaging and oxygen is complex but hopefully it would lead to the detection of truly oxygen-depleted tissue. Different ways of imaging hypoxia are discussed in this review, with nuclear medicine tracers such as [18F]-FMISO, [18F]-FAZA, or [64Cu]-ATSM but also with MRI techniques such as perfusion imaging, diffusion MRI or oxygen-enhanced MRI. Hypoxia is a pejorative factor regarding aggressiveness, tumor dissemination and resistance to treatments. Therefore, having accurate tools is particularly important.

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs.

Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters' predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan−Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p < 0.0001), low FBM3D (p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts.

Baseline 18F-FDG Metabolic Tumor Volume Predicts Response to Rituximab Induction in Post-transplant Lymphoproliferative Disorders: A Multi-institutional Retrospective Study.

Post-transplant lymphoproliferative disorder (PTLD) is a rare complication of immunosuppression. Sequential treatment is commonly proposed, combining induction with rituximab (R-induction) followed by either continuation of treatment or addition of chemotherapy depending on response. Response to R-induction, often assessed by CT scan, is a major predictor of overall survival (OS). The aim of the study was to analyze predictive factors of R-induction response, including total metabolic tumor volume (TMTV), and investigate the role of 18F-FDG PET/CT in response assessment. This retrospective multicenter study is based on patients with PTLD included in the K-VIROGREF cohort. Only patients treated by R-induction with a baseline 18F-FDG PET/CT were included. Response to R-induction was assessed by 18F-FDG PET/CT. The optimal threshold of TMTV for rituximab response was determined using receiver operating characteristic curves. Univariate and multivariate analyses were conducted to identify predictive factors of response. A total of 67 patients were included. Survival characteristics were similar to those previously reported: the complete response rate to R-induction was 30%, the 3-year OS estimate was 66%, and the treatment-related mortality was 4%. The optimal threshold for TMTV to predict R-induction response was 135 cm3. The response rate to R-induction was 38% in the 21 patients with TMTV ≥ 135 cm3 and 72% in the 46 patients with TMTV < 135 cm3. TMTV was a significant predictor of response, both at univariate and multivariate analyses (odd ratios = 3.71, P = 0.022). Baseline TMTV is predictive of response to R-induction. Early assessment of patient response is feasible with 18F-FDG PET/CT.

The impact of sarcopenia on survival and treatment tolerance in patients with head and neck cancer treated with chemoradiotherapy.

BACKGROUND: Sarcopenia appears to be a negative prognostic factor for poor survival outcomes and worse treatment tolerance in patients with head-and-neck squamous cell carcinoma (HNSCC). We evaluated sarcopenia's impact on overall survival (OS), disease-free survival (DFS) and chemo-radiation tolerance in patients with head-and-neck cancer (HNC) treated with chemoradiotherapy (CRT) from a monocentric observational study. METHODS: We identified patients with HNC treated by CRT between 2009 and 2018 with pretreatment imaging using positron emission tomography-computed tomography scans (PET/CT). Sarcopenia was measured using the pretreatment PET/CT at the L3 vertebral body using previously published methods. Clinical variables were retrospectively retrieved. RESULTS: Of 216 patients identified, 54 patients (25.47%) met the criteria for sarcopenia. These patients had a lower mean body mass index before treatment (21.92 vs. 25.65 cm/m2 , p < 0.001) and were more likely to have a history of smoking (88.89% vs. 71.52%, p = 0.01), alcohol use (55.56% vs. 38.61%, p = 0.03) and positive human papilloma virus status (67.74% vs. 41.75%, p = 0.011). At 3 years of follow-up, OS and DFS were 75% and 70% versus 82% and 85% for sarcopenic and non-sarcopenic patients, respectively (p = 0.1 and p = 0.00015). On multivariate analysis, sarcopenia appeared as a pejorative factor on DFS (hazard ratio 2.174, p = 0.0001) in the overall cohort. Sarcopenic patients did not require more chemotherapy and radiation-treatment interruptions and did not suffer from more chemo-induced and radiation-induced grade 3-4 toxicities than their non-sarcopenic counterparts. CONCLUSION: Sarcopenia in HNSCC patients is an independent adverse prognostic factor for DFS after definitive chemoradiotherapy.

Using 18F-FDG-PET/CT Metrics to Predict Survival in Ra-Dio-Iodine Refractory Thyroid Cancers.

Radio-iodine refractory (RAI-R) differentiated thyroid cancer (DTC) is a rare disease with a poor prognosis and limited therapeutic resources. Therefore, identifying prognostic factors is essential in order to select patients who could benefit from an early start of treatment. The aim of this study is to identify positron emission tomography with 18F-fluorodeoxyglucose with integrated computed tomography (18F-FDG-PET/CT) parameters to predict overall survival (OS) in patients with RAI-R DTC. In this single-center retrospective study, we analyze the 18F-FDG-PET/CT parameters of 34 patients with RAI-R DTC between April 2007 and December 2019. The parameters collected are MTV, SUVmax and progression for each site of metastasis (neck, mediastinum, lungs, liver, bone) and total sites. ROC curves, Kaplan-Meier survival analysis curves, univariate and multivariate Cox analyses determine prognostic factors for 1-year and 5-year OS. The parameters for mediastinum, liver and total sites are significantly associated with worse 1-year and 5-year OS by both ROC curve analysis and Kaplan-Meier survival analysis. Univariate Cox analysis confirms significance of mediastinum SUVmax (HR 1.08; 95% CI [1.02-1.15]; p = 0.014) and total SUVmax (HR 1.06; 95% CI [1-1.12]; p = 0.042) for worse 1-year OS; of mediastinum SUVmax (HR 1.06; 95% CI [1.02-1.10]; p = 0.003), liver SUVmax (HR 1.04; 95% CI [1.01-1.08]; p = 0.02), liver MTV (HR 2.56; 95% CI [1.13-5.82]; p = 0.025), overall SUVmax (HR 1.05; 95% CI [1.02-1.08]; p = 0.001) and total MTV (HR 1.41; 95% CI [1.07-1.86]; p = 0.016) for worse 5-year OS. Multivariate Cox analysis confirms a significant association between liver MTV (HR 1.02; 95% CI [1-1.04]; p = 0.042) and decrease 1-year OS. In this study, we demonstrate that in RAI-R DTC, 18F-FDG-PET/CT parameters of the mediastinum, liver and overall tumor burden were prognostic factors of poor 1-year and 5-year OS. Identifying these criteria could allow early therapeutic intervention in order to improve patients' survival.

Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images.

Accurate lymphoma segmentation in PET/CT images is important for evaluating Diffuse Large B-Cell Lymphoma (DLBCL) prognosis. As systemic multiple lymphomas, DLBCL lesions vary in number and size for different patients, which makes DLBCL labeling labor-intensive and time-consuming. To reduce the reliance on accurately labeled datasets, a weakly supervised deep learning method based on multi-scale feature similarity is proposed for automatic lymphoma segmentation. Weak labeling was performed by randomly dawning a small and salient lymphoma volume for the patient without accurate labels. A 3D V-Net is used as the backbone of the segmentation network and image features extracted in different convolutional layers are fused with the Atrous Spatial Pyramid Pooling (ASPP) module to generate multi-scale feature representations of input images. By imposing multi-scale feature consistency constraints on the predicted tumor regions as well as the labeled tumor regions, weakly labeled data can also be effectively used for network training. The cosine similarity, which has strong generalization, is exploited here to measure feature distances. The proposed method is evaluated with a PET/CT dataset of 147 lymphoma patients. Experimental results show that when using data, half of which have accurate labels and the other half have weak labels, the proposed method performed similarly to a fully supervised segmentation network and achieved an average Dice Similarity Coefficient (DSC) of 71.47%. The proposed method is able to reduce the requirement for expert annotations in deep learning-based lymphoma segmentation.

A tumor volume and performance status model to predict outcome before treatment in diffuse large B-cell lymphoma.

Aggressive large B-cell lymphoma (LBCL) has variable outcomes. Current prognostic tools use factors for risk stratification that inadequately identify patients at high risk of refractory disease or relapse before initial treatment. A model associating 2 risk factors, total metabolic tumor volume (TMTV) >220 cm3 (determined by fluorine-18 fluorodeoxyglucose positron emission tomography coupled with computed tomography) and performance status (PS) ≥2, identified as prognostic in 301 older patients in the REMARC trial (#NCT01122472), was validated in 2174 patients of all ages treated in 2 clinical trials, PETAL (Positron Emission Tomography-Guided Therapy of Aggressive Non-Hodgkin Lymphomas; N = 510) and GOYA (N = 1315), and in real-world clinics (N = 349) across Europe and the United States. Three risk categories, low (no factors), intermediate (1 risk factor), and high (2 risk factors), significantly discriminated outcome in most of the series. Patients with 2 risk factors had worse outcomes than patients with no risk factors in the PETAL, GOYA, and real-world series. Patients with intermediate risk also had significantly worse outcomes than patients with no risk factors. The TMTV/Eastern Cooperative Oncology Group-PS combination outperformed the International Prognostic Index with a positive C-index for progression-free survival and overall survival in most series. The combination of high TMTV > 220 cm3 and ECOG-PS ≥ 2 is a simple clinical model to identify aggressive LBCL risk categories before treatment. This combination addresses the unmet need to better predict before treatment initiation for aggressive LBCL the patients likely to benefit the most or not at all from therapy.

Characterization of positioning uncertainties in PET-CT-MR trimodality solutions for radiotherapy.

The purpose of this study was to evaluate the positioning uncertainties of two PET/CT-MR imaging setups, C1 and C2. Because the PET/CT data were acquired on the same hybrid device with automatic image registration, experiments were conducted using CT-MRI data. In C1, a transfer table was used, which allowed the patient to move from one imager to another while maintaining the same position. In C2, the patient stood up and was positioned in the same radiotherapy treatment position on each imager. The two setups provided a set of PET/CT and MR images. The accuracy of the registration software was evaluated on the CT-MRI data of one patient using known translations and rotations of MRI data. The uncertainties on the two setups were estimated using a phantom and a cohort of 30 patients. The accuracy of the positioning uncertainties was evaluated using descriptive statistics and a t-test to determine whether the mean shift significantly deviated from zero (p < 0.05) for each setup. The maximum registration errors were less than 0.97 mm and 0.6° for CT-MRI registration. On the phantom, the mean total uncertainties were less than 2.74 mm and 1.68° for C1 and 1.53 mm and 0.33° for C2. For C1, the t-test showed that the displacements along the z-axis did not significantly deviate from zero (p = 0.093). For C2, significant deviations from zero were present for anterior-posterior and superior-inferior displacements. The mean total uncertainties were less than 4 mm and 0.42° for C1 and less than 1.39 mm and 0.27° for C2 in the patients. Furthermore, the t-test showed significant deviations from zero for C1 on the anterior-posterior and roll sides. For C2, there was a significant deviation from zero for the left-right displacements.This study shows that transfer tables require careful evaluation before use in radiotherapy.

Prognostic value of total tumour volume, adding necrosis to metabolic tumour volume, in advanced or metastatic non-small cell lung cancer treated with first-line pembrolizumab.

BACKGROUND: Metabolic tumour volume (MTV) measured on fluorodeoxyglucose F18 (FDG) positron emission tomography coupled with computed tomography (PET/CT) is a prognostic factor of advanced non-small cell lung cancer (NSCLC) treated by first-line immunotherapy. However, these tumours are often necrotic and the necrosis, which is hypometabolic in PET FDG, is not included in the MTV. The aim of this study was to evaluate the prognostic value of total tumour volume (TTV), adding necrotic tumour volume (NTV) to metabolic tumour volume (MTV). METHODS: We retrospectively included 65 patients with NSCLC treated with pembrolizumab as monotherapy. All patients had a pretreatment FDG PET/CT. PET/CT measured parameters were MTV, NTV and TTV. Clinical, biological and tumour parameters were also retrieved. Receiver operator characteristics (ROC) analysis was performed and overall survival at 1 year was studied using Kaplan-Meier and uni/multivariate Cox analysis. RESULTS: In the ROC analysis, MTV, NTV, TTV, age at diagnosis, polynuclear blood neutrophil, derived neutrophil/leukocyte ratio (dNLR), and haemoglobin had an area under the curve (AUC) significantly higher than 0.5. In Kaplan-Meier analysis, prognosis was worse for patients with high MTV (p = 0.02), high TTV (p = 0.003), high NTV (p = 0.014), low haemoglobin (p < 0.001), older people (p = 0.002), neutrophil polynucleosis (p < 0.001) and dNLR (p = 0.022). All these parameters, except age and neutrophil polynucleosis, were significant prognostic factors in univariate Cox analysis (p < 0.05). In a stepwise multivariate Cox analysis focused on PET parameters, the only significant parameter was TTV (HR = 3.66, p = 0.002) and in a stepwise multivariate Cox analysis exploring all the parameters, a model combining TTV, performance status and brain metastasis was found (p = 0.002). CONCLUSIONS: TTV and NTV measured on pretreatment FDG PET/CT are significant prognosis factor for stage III-IV NSCLC treated by pembrolizumab and TTV could have a higher prognostic value than MTV.

Deep learning analysis of contrast-enhanced spectral mammography to determine histoprognostic factors of malignant breast tumours.

OBJECTIVE: To evaluate if a deep learning model can be used to characterise breast cancers on contrast-enhanced spectral mammography (CESM). METHODS: This retrospective mono-centric study included biopsy-proven invasive cancers with an enhancement on CESM. CESM images include low-energy images (LE) comparable to digital mammography and dual-energy subtracted images (DES) showing tumour angiogenesis. For each lesion, histologic type, tumour grade, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2 status, Ki-67 proliferation index, and the size of the invasive tumour were retrieved. The deep learning model used was a CheXNet-based model fine-tuned on CESM dataset. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated for the different models: images by images and then by majority voting combining all the incidences for one tumour. RESULTS: In total, 447 invasive breast cancers detected on CESM with pathological evidence, in 389 patients, which represented 2460 images analysed, were included. Concerning the ER, the deep learning model on the DES images had an AUC of 0.83 with the image-by-image analysis and of 0.85 for the majority voting. For the triple-negative analysis, a high AUC was observable for all models, in particularity for the model on LE images with an AUC of 0.90 for the image-by-image analysis and 0.91 for the majority voting. The AUC for the other histoprognostic factors was lower. CONCLUSION: Deep learning analysis on CESM has the potential to determine histoprognostic tumours makers, notably estrogen receptor status, and triple-negative receptor status. KEY POINTS: • A deep learning model developed for chest radiography was adapted by fine-tuning to be used on contrast-enhanced spectral mammography. • The adapted models allowed to determine for invasive breast cancers the status of estrogen receptors and triple-negative receptors. • Such models applied to contrast-enhanced spectral mammography could provide rapid prognostic and predictive information.

Outcomes after first-line immunochemotherapy for primary mediastinal B-cell lymphoma: a LYSA study.

Primary mediastinal B-cell lymphoma (PMBL) is a rare type of aggressive lymphoma typically affecting young female patients. The first-line standard of care remains debated. We performed a large multicenter retrospective study in 25 centers in France and Belgium to describe PMBL patient outcomes after first-line treatment in real-life settings. A total of 313 patients were enrolled and received rituximab (R) plus ACVBP (doxorubicin, cyclophosphamide, vindesine, bleomycin, and prednisone) (n = 180) or CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) delivered every 14 days (R-CHOP14, n = 76) or 21 days (R-CHOP21, n = 57) and consolidation strategies in modalities that varied according to time and institution, mainly guided by positron emission tomography. Consolidation autologous stem cell transplantation was performed for 46 (25.6%), 24 (31.6%), and 1 (1.8%) patient in the R-ACVBP, R-CHOP14, and R-CHOP21 groups, respectively (P < .001); only 17 (5.4%) patients received mediastinal radiotherapy. The end-of-treatment complete metabolic response rates were 86.3%, 86.8%, and 76.6% (P = .23) in the R-ACVBP, R-CHOP14, and R-CHOP21 groups. The median follow-up was 44 months, and the R-ACVBP, R-CHOP14, and R-CHOP21 three-year progression-free survival probabilities were 89.4% (95% confidence interval [CI], 84.8-94.2), 89.4% (95% CI, 82.7-96.6), and 74.7% (95% CI, 64-87.1) (P = .018). A baseline total metabolic tumor volume (TMTV) ≥360 cm3 was associated with a lower progression-free survival (hazard ratio, 2.18; 95% CI, 1.05-4.53). Excess febrile neutropenia (24.4% vs 5.3% vs 5.3%; P < .001) and mucositis (22.8% vs 3.9% vs 1.8%; P < .001) were observed with R-ACVBP compared with the R-CHOP regimens. Patients with PMBL treated with dose-dense immunochemotherapy without radiotherapy have excellent outcomes. R-ACVBP acute toxicity was higher than that of R-CHOP14. Our data confirmed the prognostic importance of baseline TMTV.

Angiogenesis imaging study using interim [18F] RGD-K5 PET/CT in patients with lymphoma undergoing chemotherapy: preliminary evidence.

BACKGROUND: Our aim was to measure the impact of two cycles of standard chemotherapy on tumoural neoangiogenesis by [18F] fluorine arginine-glycine-aspartic (RGD-K5) positron emission tomography-computed tomography (PET) on patients presenting with lymphoma. Nineteen patients at Rouen's Henri Becquerel Cancer Centre were prospectively included. Fluorodeoxyglucose (FDG) and RGD-K5 PET were performed before (C0) and after (C2) two cycles of chemotherapy. End-of-treatment FDG PET was performed for final evaluation. Maximum standardised uptake value (SUVmax), SUVmean, Metabolic Tumour Volume (MTV) and Angiogenic Tumour Volume (ATV) were measured for all lesions. RGD SUVmax and SUVmean were also analysed in 13 normal organs at C0 and C2. The patient's treatment response was considered using the Deauville score (DS) at the end of FDG PET treatment (DS 1-3 were considered responders, and 4 and 5 non-responders). RESULTS: Eighteen patients had both C0 FDG and RGD PET. Twelve patients had both C2 FDG and RGD, completed the treatment protocol and were included in end-of-treatment analysis. No statistical difference was found in RGD uptake of normal organs before and after chemotherapy for SUVmax and SUVmean. On C0 RGD, apart from classical Hodgkin lymphoma (cHL; n = 5) and grey zone lymphoma (GZL; n = 1), other lymphoma sub-types (n = 12) had low RGD uptake (p < 0.001). Regarding FDG, there was no significant difference for SUVmax, SUVmean and MTV at C0 and C2 between patients with cHL and non-Hodgkin lymphoma (NHL). At C2 RGD, non-responders had higher SUVmax and SUVmean compared to responders (p < 0.001). There was no significant difference in RGD ATV between responders and non-responders. CONCLUSIONS: Our study showed significant higher initial RGD uptake in patients presenting with cHL and GZL compared to NHL. Non-responder also had higher post-chemotherapy RGD uptake compared to responders. Issues raised by RGD uptake, particularly in cHL, are yet to be explored and need to be confirmed in a larger population.

Evaluation of an Automatic Classification Algorithm Using Convolutional Neural Networks in Oncological Positron Emission Tomography.

Introduction: Our aim was to evaluate the performance in clinical research and in clinical routine of a research prototype, called positron emission tomography (PET) Assisted Reporting System (PARS) (Siemens Healthineers) and based on a convolutional neural network (CNN), which is designed to detect suspected cancer sites in fluorine-18 fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT). Method: We retrospectively studied two cohorts of patients. The first cohort consisted of research-based patients who underwent PET scans as part of the initial workup for diffuse large B-cell lymphoma (DLBCL). The second cohort consisted of patients who underwent PET scans as part of the evaluation of miscellaneous cancers in clinical routine. In both cohorts, we assessed the correlation between manually and automatically segmented total metabolic tumor volumes (TMTVs), and the overlap between both segmentations (Dice score). For the research cohort, we also compared the prognostic value for progression-free survival (PFS) and overall survival (OS) of manually and automatically obtained TMTVs. Results: For the first cohort (research cohort), data from 119 patients were retrospectively analyzed. The median Dice score between automatic and manual segmentations was 0.65. The intraclass correlation coefficient between automatically and manually obtained TMTVs was 0.68. Both TMTV results were predictive of PFS (hazard ratio: 2.1 and 3.3 for automatically based and manually based TMTVs, respectively) and OS (hazard ratio: 2.4 and 3.1 for automatically based and manually based TMTVs, respectively). For the second cohort (routine cohort), data from 430 patients were retrospectively analyzed. The median Dice score between automatic and manual segmentations was 0.48. The intraclass correlation coefficient between automatically and manually obtained TMTVs was 0.61. Conclusion: The TMTVs determined for the research cohort remain predictive of total and PFS for DLBCL. However, the segmentations and TMTVs determined automatically by the algorithm need to be verified and, sometimes, corrected to be similar to the manual segmentation.

Targeted genotyping of circulating tumor DNA for classical Hodgkin lymphoma monitoring: a prospective study.

The relevance of circulating tumor DNA (ctDNA) analysis as a liquid biopsy and minimal residual disease tool in the management of classical Hodgkin Lymphoma (cHL) patients was demonstrated in retrospective settings and remains to be confirmed in a prospective setting. We developed a targeted Next-Generation sequencing (NGS) panel for fast analysis (AmpliSeq technology) of nine commonly mutated genes in biopies and ctDNA of cHL patients. We then conducted a prospective trial to assess ctDNA follow up at diagnosis and after 2 cycles of chemotherapy (C2). Sixty cHL patients treated by first line conventional chemotherapy (BEACOPPescalated [21.3%], ABVD/ABVD-like [73.5%] and other regimens [5.2%, for elderly patients] were assessed in this non-interventional study. Median age of the patients was 33.5 years (range 20-86). Variants were identified in 42 (70%) patients. Mutations of NFKBIE, TNFAIP3, STAT6, PTPN1, B2M, XPO1, ITPKB, GNA13 and SOCS1 were found in 13.3%, 31.7%, 23.3%, 5%, 33.3%, 10%, 23.3%, 13.3% and 50% of patients, respectively. ctDNA concentration and genotype are correlated with clinical characteristics and presentation. Regarding early therapeutic response, 45 patients (83%, NA=6) had a negative positron emission tomography (PET) after C2 (Deauville Score 1-3). Mean of DeltaSUVmax after C2 was -78.8%. We analyzed ctDNA after C2 for 54 patients (90%). ctDNA became rapidly undetectable in all cases after C2. Variant detection in ctDNA is suitable to depict the genetic features of cHL at diagnosis and may help to assess early treatment response, in association with PET. Clinical Trial reference: NCT02815137.