Search

Abstract Low skeletal muscle mass is a well-known prognostic factor for patients treated for a non-small-cell lung cancer by surgery or chemotherapy. However, its impact in patients treated by exclusive radiochemotherapy has never been explored. Our study tries to evaluate the prognostic value of low skeletal muscle mass and other antropometric parameters on this population. Clinical, nutritional and anthropometric date were collected for 93 patients treated by radiochemotherapy for a NSCLC. Anthropometric parameters were measured on the PET/CT by two methods. The first method was a manual segmentation at level L3, used to define Muscle Body Area (MBAL3), Visceral Fat Area (VFAL3) and Subcutaneous Fat Area (SCFAL3). The second method was an software (Anthropometer3D), allowing an automatic multislice measurement of Lean Body Mass (LBMAnthro3D), Fat Body Mass (FBMAnthro3D), Muscle Body Mass (MBMAnthro3D), Visceral Fat Mass (VFMAnthro3D), and Sub-Cutaneous Fat Mass (SCFMAnthro3D) on the PET/CT. All anthropometrics parameters were normalised by the patient's height. The primary end point was overall survival time. Univariate and then stepwise multivariate cox analysis were performed for significant parameters. Finally, Spearman's correlation between MBAL3 and MBMAnthro3D was assessed. Forty-one (44%) patients had low skeletal muscle mass. The median overall survival was 18 months for low skeletal muscle mass patients versus 36 months for non-low skeletal muscle mass patients (p = 0.019). Low skeletal muscle mass (HR = 1.806, IC95% [1.09–2.98]), serums albumin level

Abstract Objectives To propose an easily applicable segmentation method (perPET-RT) for delineation of tumour volume during radiotherapy on interim fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with non-small cell lung cancer (NSCLC). Material and methods Sixty-seven patients (51 primary tumours, 60 lymph nodes), from 4 prospective studies, underwent an FDG PET/CT scan during the fifth week of radiation therapy, using different generations of PET/CT. Per-therapeutic PET/CT scans were delineated in consensus by two experienced physicians leading to the gold standard threshold to be applied. The mathematical expression of Thopt, the optimal threshold to be applied as a function of the maximum standard uptake value (SUVmax), was determined. The performance of this method (perPET-RT) was assessed by computing the DICE similarity coefficient (DSC) and was compared with 8 fixed threshold values and 3 adaptive thresholding methods. Results Thopt verified the following expression: Thopt = A.ln(1/SUVmax) + B where A and B were 2 constants. A and B were independent from the generation of PET/CT, but depended on the type of lesions (primary lung tumours vs. lymph nodes). PerPET-RT showed good to very good agreement in comparison to the gold standard. The mean and standard deviation of DSC value was 0.81 ± 0.13 for lung lesions and 0.78 ± 0.15 for lymph nodes. PerPET-RT showed a significant better agreement than the other segmentation methods (p

Background and Purpose: To promote the development of auto-segmentation methods for head and neck (HaN) radiation treatment (RT) planning that exploit the information of computed tomography (CT) and magnetic resonance (MR) imaging modalities, we organized HaN-Seg: The Head and Neck Organ-at-Risk CT and MR Segmentation Challenge., Materials and Methods: The challenge task was to automatically segment 30 organs-at-risk (OARs) of the HaN region in 14 withheld test cases given the availability of 42 publicly available training cases. Each case consisted of one contrast-enhanced CT and one T1-weighted MR image of the HaN region of the same patient, with up to 30 corresponding reference OAR delineation masks. The performance was evaluated in terms of the Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD 95 ), and statistical ranking was applied for each metric by pairwise comparison of the submitted methods using the Wilcoxon signed-rank test., Results: While 23 teams registered for the challenge, only seven submitted their methods for the final phase. The top-performing team achieved a DSC of 76.9 % and a HD 95 of 3.5 mm. All participating teams utilized architectures based on U-Net, with the winning team leveraging rigid MR to CT registration combined with network entry-level concatenation of both modalities., Conclusion: This challenge simulated a real-world clinical scenario by providing non-registered MR and CT images with varying fields-of-view and voxel sizes. Remarkably, the top-performing teams achieved segmentation performance surpassing the inter-observer agreement on the same dataset. These results set a benchmark for future research on this publicly available dataset and on paired multi-modal image segmentation in general., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Thoracic radiation intensification is debated in patients with stage III non-small-cell lung cancer (NSCLC). We aimed to assess the activity and safety of a boost radiotherapy dose up to 74 Gy in a functional sub-volume given according to on-treatment [ 18 F]fluorodeoxyglucose ([ 18 F]FDG)-PET results., Methods: In this multicentre, randomised, controlled non-comparative phase 2 trial, we recruited patients aged 18 years or older with inoperable stage III NSCLC without EGFR mutation or ALK rearrangement with an Eastern Cooperative Oncology Group performance status of 0-1, and who were affiliated with or a beneficiary of a social benefit system, with evaluable tumour or node lesions, preserved lung function, and who were amenable to curative-intent radiochemotherapy. Patients were randomly allocated using a central interactive web-response system in a non-masked method (1:1; minimisation method used [random factor of 0·8]; stratified by radiotherapy technique [intensity-modulated radiotherapy vs three-dimensional conformal radiotherapy] and by centre at which patients were treated) either to the experimental adaptive radiotherapy group A, in which only patients with positive residual metabolism on [ 18 F]FDG-PET at 42 Gy received a boost radiotherapy (up to 74 Gy in 33 fractions), with all other patients receiving standard radiotherapy dosing (66 Gy in 33 fractions over 6·5 weeks), or to the standard radiotherapy group B (66 Gy in 33 fractions) over 6·5 weeks. All patients received two cycles of induction platinum-based chemotherapy cycles (paclitaxel 175 mg/m 2 intravenously once every 3 weeks and carboplatin area under the curve [AUC]=6 once every 3 weeks, or cisplatin 80 mg/m 2 intravenously once every 3 weeks and vinorelbine 30 mg/m 2 intravenously on day 1 and 60 mg/m 2 orally [or 30 mg/m 2 intravenously] on day 8 once every 3 weeks). Then they concomitantly received radiochemotherapy with platinum-based chemotherapy (three cycles for 8 weeks, with once per week paclitaxel 40 mg/m 2 intravenously and carboplatin AUC=2 or cisplatin 80 mg/m 2 intravenously and vinorelbine 20 mg/m 2 intravenously on day 1 and 40 mg/m 2 orally (or 20 mg/m 2 intravenously) on day 8 in 21-day cycles). The primary endpoint was the 15-month local control rate in the eligible patients who received at least one dose of concomitant radiochemotherapy. This RTEP7-IFCT-1402 trial is registered with ClinicalTrials.gov (NCT02473133), and is ongoing., Findings: From Nov 12, 2015, to July 7, 2021, we randomly assigned 158 patients (47 [30%] women and 111 [70%] men) to either the boosted radiotherapy group A (81 [51%]) or to the standard radiotherapy group B (77 [49%)]. In group A, 80 (99%) patients received induction chemotherapy and 68 (84%) received radiochemotherapy, of whom 48 (71%) with residual uptake on [ 18 F]FDG-PET after 42 Gy received a radiotherapy boost. In group B, all 77 patients received induction chemotherapy and 73 (95%) received radiochemotherapy. At the final analysis, the median follow-up for eligible patients who received radiochemotherapy (n=140) was 45·1 months (95% CI 39·3-48·3). The 15-month local control rate was 77·6% (95% CI 67·6-87·6%) in group A and 71·2% (95% CI 60·8-81·6%) in group B. Acute (within 90 days from radiochemotherapy initiation) grade 3-4 adverse events were observed in 20 (29%) of 68 patients in group A and 33 (45%) of 73 patients in group B, including serious adverse events in five (7%) patients in group A and ten (14%) patients in group B. The most common grade 3-4 adverse events were febrile neutropenia (seven [10%] of 68 in group A vs 16 [22%] of 73 in group B), and anaemia (five [7%] vs nine [12%]). In the acute phase, two deaths (3%) occurred in group B (one due to a septic shock related to chemotherapy, and the other due to haemotypsia not related to study treatment), and no deaths occurred in group A. After 90 days, one additional treatment-unrelated death occurred in group A and two deaths events occurred in group B (one radiation pneumonitis and one pneumonia unrelated to treatment)., Interpretation: A thoracic radiotherapy boost, based on interim [ 18 F]FDG-PET, led to a meaningful local control rate with no difference in adverse events between the two groups in organs at risk, in contrast with previous attempts at thoracic radiation intensification, warranting a randomised phase 3 evaluation of such [ 18 F]FDG-PET-guided radiotherapy dose adaptation in patients with stage III NSCLC., Funding: Programme Hospitalier de Recherche Clinique National 2014., Competing Interests: Declaration of interests EM declared payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Varian Medical Systems, AstraZeneca, MSD, Janssen, and Sanofi; support for attending meetings or travel, or both, from Ipsen, Janssen, and AstraZeneca; and participation on a data safety monitoring board or advisory board for AstraZeneca and Janssen. IB-R declares payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Novartis 3A and Curium; payment for expert testimony from Curium; and support for attending meetings or travel, or both, from Novartis 3A. CLP declares payment or honoraria to her institution for lectures, presentations, speakers bureaus, manuscript writing, or educational events from AstraZeneca and Amgen; support for attending meetings or travel, or both, paid to her institution from Janssen and Roche; and participation on a data safety monitoring board or advisory board paid to her institution for AstraZeneca, Varian, MSD, and Roche. EG-L declares payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Amgen, AstraZeneca, Ipsen, Janssen, Lilly, MSD, Novartis, Pfizer, Sanofi, and Takeda; and support for attending meetings or travel, or both, from Takeda, MSD, AstraZeneca, and Roche. CA-V declares support for attending meetings or travel, or both, from Roche, Sanofi, MSD, BMS, Lilly, Pfizer, AstraZeneca, Janssen, and Abbvie; and participation on a data safety monitoring board or advisory board for Roche, Sanofi, MSD, BMS, Lilly, Pfizer, AstraZeneca, Janssen, and Abbvie. ALan and EA are employees of the French Intergroup (Intergroupe Francophone de Cancérologie Thoracique). GZ declares consulting fees paid to his institution from AstraZeneca, BMS, Takeda, and Roche; payment or honoraria to his institution for lectures, presentations, speakers bureaus, manuscript writing, or educational events from AstraZeneca, Inventiva, BMS, and Pfizer; support for attending meetings or travel, or both, from Roche, BMS, Abbvie, Pfizer, and AstraZeneca; and receipt of equipment, materials, drugs, medical writing, gifts, or other services to his institution by Foundation Roche. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

This prospective study aimed to investigate the prognostic effect of sarcopenia, geriatric, and nutritional status in older patients with diffuse large B-cell lymphoma (DLBCL). Ninety-five patients with DLBCL older than 70 years who were treated with immunochemotherapy were included. The lumbar L3 skeletal muscle index (L3-SMI) was measured by computed tomography at baseline, and sarcopenia was defined as low L3-SMI. Geriatric assessment included G8 score, CIRS-G scale, Timed Up and Go test, and instrumental activity of daily living. Nutritional status was assessed using the Mini Nutritional Assessment and the body mass index, and several scores used in the literature incorporating nutritional and inflammatory biomarkers, namely the Nutritional and inflammatory status (NIS), Geriatric Nutritional Risk Index, Prognostic Nutritional Index, and Glasgow Prognostic Score.Fifty-three patients were considered sarcopenic. Sarcopenic patients displayed higher levels of inflammation markers and lower levels of prealbumin than non-sarcopenic patients. Sarcopenia was associated with NIS, but was not associated with severe adverse events and treatment disruptions. They were, however, more frequent among patients with elevated NIS. Sarcopenia did not appear in this study as a prognostic factor for progression-free survival (PFS) or overall survival (OS). However, NIS emerged as predictive of the outcome with a 2-year PFS rate of 88% in the NIS ≤ 1 group and 49% in the NIS > 1 group and a significant effect in a multivariate analysis for both PFS (p = 0.049) and OS (HR = 9.61, CI 95% = [1.03-89.66], p = 0.04). Sarcopenia was not associated with adverse outcomes, but was related to NIS, which appeared to be an independent prognostic factor., (© 2023. The Author(s).)

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., Competing Interests: Declaration of competing interest No conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Purpose: The delineation of target volumes and organs at risk is the main source of uncertainty in radiation therapy. Numerous interobserver variability (IOV) studies have been conducted, often with unclear methodology and nonstandardized reporting. We aimed to identify the parameters chosen in conducting delineation IOV studies and assess their performances and limits., Methods and Materials: We conducted a systematic literature review to highlight major points of heterogeneity and missing data in IOV studies published between 2018 and 2021. For the main used metrics, we did in silico analyses to assess their limits in specific clinical situations., Results: All disease sites were represented in the 66 studies examined. Organs at risk were studied independently of tumor site in 29% of reviewed IOV studies. In 65% of studies, statistical analyses were performed. No gold standard (GS; ie, reference) was defined in 36% of studies. A single expert was considered as the GS in 21% of studies, without testing intraobserver variability. All studies reported both absolute and relative indices, including the Dice similarity coefficient (DSC) in 68% and the Hausdorff distance (HD) in 42%. Limitations were shown in silico for small structures when using the DSC and dependence on irregular shapes when using the HD. Variations in DSC values were large between studies, and their thresholds were inconsistent. Most studies (51%) included 1 to 10 cases. The median number of observers or experts was 7 (range, 2-35). The intraclass correlation coefficient was reported in only 9% of cases. Investigating the feasibility of studying IOV in delineation, a minimum of 8 observers with 3 cases, or 11 observers with 2 cases, was required to demonstrate moderate reproducibility., Conclusions: Implementation of future IOV studies would benefit from a more standardized methodology: clear definitions of the gold standard and metrics and a justification of the tradeoffs made in the choice of the number of observers and number of delineated cases should be provided., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Intratumoral hypoxia is associated with a poor prognosis and poor response to treatment in head and neck cancers. Its identification would allow for increasing the radiation dose to hypoxic tumor subvolumes. 18F-FMISO PET imaging is the gold standard; however, quantitative multiparametric MRI could show the presence of intratumoral hypoxia. Thus, 16 patients were prospectively included and underwent 18F-FDG PET/CT, 18F-FMISO PET/CT, and multiparametric quantitative MRI (DCE, diffusion and relaxometry T1 and T2 techniques) in the same position before treatment. PET and MRI sub-volumes were segmented and classified as hypoxic or non-hypoxic volumes to compare quantitative MRI parameters between normoxic and hypoxic volumes. In total, 13 patients had hypoxic lesions. The Dice, Jaccard, and overlap fraction similarity indices were 0.43, 0.28, and 0.71, respectively, between the FDG PET and MRI-measured lesion volumes, showing that the FDG PET tumor volume is partially contained within the MRI tumor volume. The results showed significant differences in the parameters of SUV in FDG and FMISO PET between patients with and without measurable hypoxic lesions. The quantitative MRI parameters of ADC, T1 max mapping and T2 max mapping were different between hypoxic and normoxic subvolumes. Quantitative MRI, based on free water diffusion and T1 and T2 mapping, seems to be able to identify intra-tumoral hypoxic sub-volumes for additional radiotherapy doses.

Background: Molecular factors influence relapse patterns in glioblastoma. The hotspot mutation located at position 289 of the extracellular domain of the epidermal growth factor receptor (EGFR A289mut ) is associated with a more infiltrative phenotype. The primary objective of this study was to explore the impact of the EGFR A289 mutation on the pattern of relapse after chemoradiotherapy-based treatment of patients suffering from newly diagnosed glioblastoma., Patients and Methods: An ancillary study from a prospective cohort of patients suffering from glioblastoma was conducted. All patients received radiotherapy and concomitant temozolomide. The population was divided into two groups according to EGFR A289 status (mutated versus wild-type). The primary endpoint was the overlap score (varying from 0 to 1) between the initial irradiated tumor volume (Vinit) and the relapse volume (Vr). Secondary endpoints explored the impact of EGFR A289mut on survival., Results: One hundred twenty-eight patients were included and analyzed: 11% had EGFR A289mut glioblastoma (n = 14/128). EGFR A289mut glioblastomas had a relapse pattern that was more marginal than EGFR A289wt glioblastomas: a median overlap score Vinit/Vr of 0.96 was observed in the EGFR A289mut group versus 1 in the EGFR A289wt group (P = 0.05). Half of the population with EGFR A289mut tumor (n = 7/14) had a marginal relapse (i.e. overlap score Vr/Vinit ≤ 0.95) compared to 23.7% (n = 27/114) in the EGFR A289wt group, P = 0.035. EGFR A289mut did not influence survival., Conclusion: We highlighted a link between the EGFR A289 mutation and the relapse pattern in glioblastoma. The independent role of EGFR A289mut and its clinical implication should now be explored in further studies., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The aim of the study was to prospectively evaluate different biomarkers to identify the most reliable for anticipating complications after major abdominal surgery for digestive cancer in older patients and compare their performance to the existing definition and screening algorithm of sarcopenia from EWGSOP., Methods: Ninety-five consecutive patients aged over 65 years who underwent elective surgery for digestive cancer were prospectively included in the SAXO study. Sarcopenia was defined according to EWGSOP criteria (four level from no sarcopenia to severe sarcopenia). Strength and physical performance were evaluated with the handgrip test (HGT) and gait speed test (GST), respectively. CT scan analysis was used to calculate the skeletal muscle index (SMI), intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). Measures were adjusted to body mass index (BMI). Complication grading was performed using the Clavien‒Dindo classification. A doubly robust estimator with multivariable regression was used to limit bias., Results: Sixteen patients presented with sarcopenia. Adjusted to BMI, sarcopenic patients had an increased IMAT BMI (0.35 vs. 0.22; p = 0.003) and increased VAT BMI (7.85 vs. 6.13; p = 0.048). In multivariable analysis, IMAT was an independent risk factor for minor and severe complications (OR = 1.298; 95% CI [1.031: 1.635] p = 0.027), while an increased SAT area was a protective factor (OR = 0.982; 95% CI [0.969: 0.995] p = 0.007). Twenty-two patients were obese (BMI ≥30 kg/m 2 ). While no association was observed between obesity and sarcopenia (according to EWGSOP definition), obese patients had increased IMAT BMI (0.31 vs. 0.23; p = 0.010) and VAT BMI (8.40 vs. 6.49; p = 0.019). The combination of SAT, VAT and IMAT performed well to anticipate severe complication (AUC = 0.759) while AUC of EWGSOP 2010 and 2019 algorithm were 0.660 and 0.519, respectively., Discussion: Non-invasive and imaging related measures of IMAT, SAT and VAT seems to be valuable tools to refine risk-assessment of older patients in surgery and specially to detect myosteatosis in obese ones., Competing Interests: Declaration of competing interest None of the authors has a conflict of interest to declare., (Copyright © 2022. Published by Elsevier Ltd.)

Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To this end, radiomics has been proposed as a field of study where images are used instead of invasive methods. The first step in radiomic analysis in oncology is lesion segmentation. However, this task is time consuming and can be physician subjective. Automated tools based on supervised deep learning have made great progress in helping physicians. However, they are data hungry, and annotated data remains a major issue in the medical field where only a small subset of annotated images are available., Methods: In this work, we propose a multi-task, multi-scale learning framework to predict patient's survival and response. We show that the encoder can leverage multiple tasks to extract meaningful and powerful features that improve radiomic performance. We also show that subsidiary tasks serve as an inductive bias so that the model can better generalize., Results: Our model was tested and validated for treatment response and survival in esophageal and lung cancers, with an area under the ROC curve of 77% and 71% respectively, outperforming single-task learning methods., Conclusions: Multi-task multi-scale learning enables higher performance of radiomic analysis by extracting rich information from intratumoral and peritumoral regions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Despite significant therapeutic advances in the treatment of locally advanced inoperable non-small cell lung cancer (NSCLC), notably through adjuvant immunotherapy, the rate of therapeutic failure remains high. The use of positron emission tomography with fluorodeoxyglucose (FDG-PET), respiratory motion and intensity modulated radiotherapy (IMRT) have led to therapeutic improvements with reduced toxicity and better local control. The optimal dose to be delivered remains unknown due to discordant results of studies for almost 20 years and the way to define the area to benefit from a dose increase (whole volume, subvolume defined by pre- or per-radiotherapy PET)., (Copyright © 2022. Published by Elsevier Masson SAS.)

Gestational diabetes mellitus (GDM), which is defined as a state of hyperglycemia that is first recognized during pregnancy, is currently the most common medical complication in pregnancy. GDM affects approximately 15% of pregnancies worldwide, accounting for approximately 18 million births annually. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both the mother and infant. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness. A deeper understanding of the risk factors and pathogenesis becomes a necessity, with particular emphasis on the influence of SARS-CoV-2 and diagnostics, as well as an effective treatment, which may reduce perinatal and metabolic complications. The primary treatments for GDM are diet and increased exercise. Insulin, glibenclamide and metformin can be used to intensify the treatment. This paper provides an overview of the latest reports on the epidemiology, pathogenesis, diagnosis and treatment of GDM based on the literature.

The aim of the study was to compare the perception of stress and the characteristic coping-strategies among students in the context of the different anti-pandemic measures taken in Belarus, Poland, and the Russian exclave of Kaliningrad. A cross-sectional online survey using standardized questionnaires (Perceived Stress Scale-PSS-10 and Brief-COPE-Mini-COPE inventory) was conducted among 3113 students of seven universities in three neighboring regions on both sides of the eastern border of the EU. The groups that are the most prone to stress are the Polish and Russians students. Among the students from Belarus, 122 (13.7%) have high levels of stress symptoms. Among the respondents from Poland-238 (19.4%), and 191 (19.2%) from Kaliningrad have high levels of stress, respectively. The different approaches of the authorities to the COVID-19 pandemic diversified the choice of students' stress coping strategies. The behavior of the students from Kaliningrad and Poland was similar. The Belarusian students used active coping strategies less often, while an avoidance-focused style, and denial were more frequent. The neglect of restrictive anti-pandemic measures by the Belarusian students was manifested by a higher incidence of disease and minimal use of vaccinations.

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., (© 2022. The Author(s), under exclusive licence to European Society of Radiology.)

Background: PET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acquired at 25% of the count statistics considering US practices. Our aim is to determine the limits of a deep learning based denoising algorithm (SubtlePET) applied to statistically reduced PET raw data from 3 different last generation PET scanners in comparison to the regular acquisition in phantom and patients, considering the European guidelines for radiotracer injection activities. Images of low and high contrasted (SBR = 2 and 5) spheres of the IEC phantom and high contrast (SBR = 5) of micro-spheres of Jaszczak phantom were acquired on 3 different PET devices. 110 patients with different pathologies were included. The data was acquired in list-mode and retrospectively reconstructed with the regular acquisition count statistic (PET100), 50% reduction in counts (PET50) and 66% reduction in counts (PET33). These count reduced images were post-processed with SubtlePET to obtain PET50 + SP and PET33 + SP images. Patient image quality was scored by 2 senior nuclear physicians. Peak-signal-to-Noise and Structural similarity metrics were computed to compare the low count images to regular acquisition (PET100)., Results: SubtlePET reliably denoised the images and maintained the SUV max values in PET50 + SP. SubtlePET enhanced images (PET33 + SP) had slightly increased noise compared to PET100 and could lead to a potential loss of information in terms of lesion detectability. Regarding the patient datasets, the PET100 and PET50 + SP were qualitatively comparable. The SubtlePET algorithm was able to correctly recover the SUV max values of the lesions and maintain a noise level equivalent to full-time images., Conclusion: Based on our results, SubtlePET is adapted in clinical practice for half-time or half-dose acquisitions based on European recommended injected dose of 3 MBq/kg without diagnostic confidence loss., (© 2022. The Author(s).)

It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation.

In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis-Havrda-Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head-neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis-Havrda-Charvat cross-entropy is a parameterized cross-entropy with the parameter α. Shannon entropy is a particular case of Tsallis-Havrda-Charvat entropy for α=1. The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head-neck cancers and 146 from lung cancers. The results show that Tsallis-Havrda-Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α.

Hypoxic areas are typically resistant to treatment. However, the fluorine-18-fluoroazomycin-arabinoside (FAZA) and fluorine 18 misonidazole (FMISO) tracers have never been compared in non small cell lung cancer (NSCLC). This study compares the capability of 18F-FAZA PET/CT with that of 18F-FMISO PET/CT for detecting hypoxic tumour regions in early and locally advanced NSCLC patients. We prospectively evaluated patients who underwent preoperative PET scans before surgery for localised NSCLC (i.e., fluorodeoxyglucose (FDG)-PET, FMISO-PET, and FAZA-PET). The PET data of the three tracers were compared with each other and then compared to immunohistochemical analysis (GLUT-1, CAIX, LDH-5, and HIF1-Alpha) after tumour resection. Overall, 19 patients with a mean age of 68.2 ± 8 years were included. There were 18 lesions with significant uptake (i.e., SUVmax >1.4) for the F-MISO and 17 for FAZA. The mean SUVmax was 3 (±1.4) with a mean volume of 25.8 cc (±25.8) for FMISO and 2.2 (±0.7) with a mean volume of 13.06 cc (±13.76) for FAZA. The SUVmax of F-MISO was greater than that of FAZA ( p = 0.0003). The SUVmax of F-MISO shows a good correlation with that of FAZA at 0.86 (0.66-0.94). Immunohistochemical results are not correlated to hypoxia PET regardless of the staining. The two tracers show a good correlation with hypoxia, with FMISO being superior to FAZA. FMISO, therefore, remains the reference tracer for defining hypoxic volumes.

Low skeletal muscle mass is a well-known prognostic factor for patients treated for a non-small-cell lung cancer by surgery or chemotherapy. However, its impact in patients treated by exclusive radiochemotherapy has never been explored. Our study tries to evaluate the prognostic value of low skeletal muscle mass and other antropometric parameters on this population. Clinical, nutritional and anthropometric date were collected for 93 patients treated by radiochemotherapy for a NSCLC. Anthropometric parameters were measured on the PET/CT by two methods. The first method was a manual segmentation at level L3, used to define Muscle Body Area (MBA L3 ), Visceral Fat Area (VFA L3 ) and Subcutaneous Fat Area (SCFA L3 ). The second method was an software (Anthropometer3D), allowing an automatic multislice measurement of Lean Body Mass (LBM Anthro3D ), Fat Body Mass (FBM Anthro3D ), Muscle Body Mass (MBM Anthro3D ), Visceral Fat Mass (VFM Anthro3D ), and Sub-Cutaneous Fat Mass (SCFM Anthro3D ) on the PET/CT. All anthropometrics parameters were normalised by the patient's height. The primary end point was overall survival time. Univariate and then stepwise multivariate cox analysis were performed for significant parameters. Finally, Spearman's correlation between MBA L3 and MBM Anthro3D was assessed. Forty-one (44%) patients had low skeletal muscle mass. The median overall survival was 18 months for low skeletal muscle mass patients versus 36 months for non-low skeletal muscle mass patients (p = 0.019). Low skeletal muscle mass (HR = 1.806, IC95% [1.09-2.98]), serums albumin level < 35 g/l (HR = 2.203 [1.19-4.09]), Buzby Index < 97.5 (HR = 2.31 [1.23-4.33]), WHO score = 0 (HR = 0.59 [0.31-0.86] and MBM Anthro3D  < 8.56 kg/m 2 (HR = 2.36 [1.41-3.90]) were the only significant features in univariates analysis. In the stepwise multivariate Cox analysis, only MBM Anthro3D  < 8.56 kg/m 2 (HR = 2.16, p = 0.003) and WHO score = 0 (HR = 0.59, p = 0.04) were significant. Finally, muscle quantified by MBA L3 and MBM Anthro3D were found to be highly correlated (Spearman = 0.9). Low skeletal muscle mass, assessed on the pre-treatment PET/CT is a powerful prognostic factor in patient treated by radiochemotherapy for a NSCLC. The automatic software Anthropometer3D can easily identify patients a risk that could benefit an adapted therapy.

Glioblastomas (GBMs) are the most common primary brain tumors characterized by strong invasiveness and angiogenesis. GBM cells and microenvironment secrete angiogenic factors and also express chemoattractant G protein-coupled receptors (GPCRs) to their advantage. We investigated the role of the vasoactive peptide urotensin II (UII) and its receptor UT on GBM angiogenesis and tested potential ligand/therapeutic options based on this system. On glioma patient samples, the expression of UII and UT increased with the grade with marked expression in the vascular and peri-necrotic mesenchymal hypoxic areas being correlated with vascular density. In vitro human UII stimulated human endothelial HUV-EC-C and hCMEC/D3 cell motility and tubulogenesis. In mouse-transplanted Matrigel sponges, mouse (mUII) and human UII markedly stimulated invasion by macrophages, endothelial, and smooth muscle cells. In U87 GBM xenografts expressing UII and UT in the glial and vascular compartments, UII accelerated tumor development, favored hypoxia and necrosis associated with increased proliferation (Ki67), and induced metalloproteinase (MMP)-2 and -9 expression in Nude mice. UII also promoted a "tortuous" vascular collagen-IV expressing network and integrin expression mainly in the vascular compartment. GBM angiogenesis and integrin αvβ3 were confirmed by in vivo 99m Tc-RGD tracer imaging and tumoral capture in the non-necrotic area of U87 xenografts in Nude mice. Peptide analogs of UII and UT antagonist were also tested as potential tumor repressor. Urotensin II-related peptide URP inhibited angiogenesis in vitro and failed to attract vascular and inflammatory components in Matrigel in vivo . Interestingly, the UT antagonist/biased ligand urantide and the non-peptide UT antagonist palosuran prevented UII-induced tubulogenesis in vitro and significantly delayed tumor growth in vivo. Urantide drastically prevented endogenous and UII-induced GBM angiogenesis, MMP, and integrin activations, associated with GBM tumoral growth. These findings show that UII induces GBM aggressiveness with necrosis and angiogenesis through integrin activation, a mesenchymal behavior that can be targeted by UT biased ligands/antagonists., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Le Joncour, Guichet, Dembélé, Mutel, Campisi, Perzo, Desrues, Modzelewski, Couraud, Honnorat, Ferracci, Marguet, Laquerrière, Vera, Bohn, Langlois, Morin, Gandolfo and Castel.)

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 ( 18 F-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., Competing Interests: LS and VS are employees of the company Siemens Healthineers. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Pinochet, Eude, Becker, Shah, Sibille, Toledano, Modzelewski, Vera and Decazes.)

This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Among the available imaging techniques, functional imaging provided by nuclear medicine departments represents a tool of choice for the oncoradiotherapist for targeting tumour activity, with positron emission tomography as the main modality. Before, during or after radiotherapy, functional imaging helps guide the oncoradiotherapist in making decisions and in the strategic choice of pathology management. Setting up a working group to ensure perfect coordination at all levels is the first step. Key points for a common and coordinated management between the two departments are the definition of an organizational logistic, training of personnel at every levels, standardization of nomenclatures, the choice of adapted and common equipment, implementation of regulatory controls, and research/clinical routine continuum. The availability of functional examinations dedicated to radiotherapy in clinical routine is possible and requires a convergence of teams and a pooling of tools and techniques., (Copyright © 2020 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

The assessment of tumour response during and after radiotherapy determines the subsequent management of patients (adaptation of treatment plan, monitoring, adjuvant treatment, rescue treatment or palliative care). In addition to its role in extension assessment and therapeutic planning, positron emission tomography combined with computed tomography provides useful functional information for the evaluation of tumour response. The objective of this article is to review published data on positron emission tomography combined with computed tomography as a tool for evaluating external radiotherapy for cancers. Data on positron emission tomography combined with computed tomography scans acquired at different times (during, after initial and after definitive [chemo-]radiotherapy, during post-treatment follow-up) in solid tumours (lung, head and neck, cervix, oesophagus, prostate and rectum) were collected and analysed. Recent recommendations of the National Comprehensive Cancer Network are also reported. Positron emission tomography combined with computed tomography with ( 18 F)-labelled fluorodeoxyglucose has a well-established role in clinical routine after chemoradiotherapy for locally advanced head and neck cancers, particularly to limit the number of neck lymph node dissection. This imaging modality also has a place for the evaluation of initial chemoradiotherapy of oesophageal cancer, including the detection of distant metastases, and for the post-therapeutic evaluation of cervical cancer. Several radiotracers for positron emission tomography combined with computed tomography, such as choline, are also recommended for patients with prostate cancer with biochemical failure. ( 18 F)-fluorodeoxyglucose positron emission tomography combined with computed tomography is optional in many other circumstances and its clinical benefits, possibly in combination with MRI, to assess response to radiotherapy remain a very active area of research., (Copyright © 2020 Société française de radiothérapie oncologique (SFRO). Published by Elsevier Masson SAS. All rights reserved.)

Purpose: The high rates of failure in the radiotherapy target volume suggest that patients with stage II or III non-small-cell lung cancer (NSCLC) should receive an increased total dose of radiotherapy. 2-Deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) and [ 18 F]fluoromisonidazole ([ 18 F]FMISO) (hypoxia) uptake on pre-radiotherapy positron emission tomography (PET)/X-ray computed tomography (CT) have been independently reported to identify intratumor subvolumes at higher risk of relapse after radiotherapy. We have compared the [ 18 F]FDG and [ 18 F]FMISO volumes defined by PET/CT in NSCLC patients included in a prospective study., Procedures: Thirty-four patients with non-resectable lung cancer underwent [ 18 F]FDG and [ 18 F]FMISO PET/CT before (pre-RT) and during radiotherapy (around 42 Gy, per-RT). The criteria were to delineate 40 % and 90 % SUVmax thresholds on [ 18 F]FDG PET/CT (metabolic volumes), and SUV > 1.4 on pre-RT [ 18 F]FMISO PET/CT (hypoxic volume). The functional volumes were delineated within the tumor volume as defined on co-registered CTs., Results: The mean pre-RT and per-RT [ 18 F]FDG volumes were not statistically different (30.4 cc vs 22.2; P = 0.12). The mean pre-RT SUVmax [ 18 F]FDG was higher than per-RT SUVmax (12.7 vs 6.5; P < 0.0001). The mean [ 18 F]FMISO SUVmax and volumes were 2.7 and 1.37 cc, respectively. Volume-based analysis showed good overlap between [ 18 F]FDG and [ 18 F]FMISO for all methods of segmentation but a poor correlation for Jaccard or Dice Indices (DI). The DI maximum was 0.45 for a threshold at 40 or 50 %., Conclusion: The correlation between [ 18 F]FDG and [ 18 F]FMISO uptake is low in NSCLC, making it possible to envisage different management strategies as the studies in progress show.

Background: Sarcopenia is defined by a loss of skeletal muscle mass with or without loss of fat mass. Sarcopenia has been associated to reduced tolerance to treatment and worse prognosis in cancer patients, including patients undergoing surgery for limited oesophageal cancer. Concomitant chemo-radiotherapy is the standard treatment for locally-advanced tumour, not accessible to surgical resection. Using automated delineation of the skeletal muscle, we have investigated the prognostic value of sarcopenia in locally advanced oesophageal cancer (LAOC) patients treated by curative-intent chemo-radiotherapy., Methods: The clinical, nutritional, anthropometric, and functional-imaging ( 18 FDG-PET/CT) data were collected in 97 patients treated between 2006 and 2012 in our institution. The skeletal muscle area was automatically delineated on cross-sectional CT images acquired at the 3rd. lumbar vertebra level and divided by the patient's squared height (SML3/h 2 ) to obtain the Skeletal Muscle Index (SMI). The primary endpoint was overall survival probability., Results: Seventy-six deaths were reported. The median survival time was 27 [95% Confidence Interval 23-40] months for the whole population. Univariate analyses (Cox Proportional Hazard Model) showed decreased survival probabilities in patients with reduced SMI, WHO > 0, Body Mass Index ≤21, and Nutritional Risk Index ≤97.5. Multivariate analyses showed that sarcopenia was the only significant prognostic factor (HR 2.32 [1.24-4.34], p = 0.008). Using Receiver Operating Characteristics curves, the Area Under the Curve (AUC) was 0.73 in males (p = 0.0002], the optimal threshold being 51.5 cm 2 /m 2 . In women, the AUC was 0.65 (p = 0.19)., Conclusion: Sarcopenia is a powerful independent prognostic factor, associated with a rise of the overall mortality in patients treated exclusively by radiochemotherapy for a locally advanced oesophageal cancer. L3 CT images are easily gathered from 18 FDG-PET/CT acquisitions.

Background and Objectives: The global epidemic of diabetes, especially type 2 (DM2), is related to lifestyle changes, obesity, and the process of population aging. Diabetic retinopathy (DR) is the most serious complication of the eye caused by diabetes. The aim of this research was to assess the prevalence of diabetic retinopathy in type 1 and type 2 diabetes mellitus patients in north-east Poland. Materials and Methods : The eye fundus was assessed on the basis of two-field 50 degrees color fundus photographs that showed the optic nerve and macula in the center after the pupil was dilated with 1% tropicamide. Results : The experimental group included 315 (26%) patients with type 1 diabetes mellitus (DM1) and 894 (74%) patients with DM2. DM1 patients were diagnosed with DR in 32.58% of cases, with non-proliferative diabetic retinopathy (NPDR) in 24.44% of cases, proliferative diabetic retinopathy (PDR) in 1.59% of cases, diabetic macular edema (DME) in 5.40% of cases, and PDR with DME in 0.95% of cases. DR was found in DM2 patients in 23.04% of cases, NPDR in 17.11% of cases, PDR in 1.01% of cases, DME in 4.81% of cases, and PDR with DME in 0.11% of cases. Conclusions : The presented study is the first Polish study on the prevalence of diabetic retinopathy presenting a large group of patients, and its results could be extrapolated to the whole country. Diabetic retinopathy was found in 25.48% of patients in the whole experimental group. The above results place Poland within the European average, indicating the quality of diabetic care offered in Poland, based on the number of observed complications., Competing Interests: The authors declare no conflict of interest.