Prognostic relevance of sarcopenia, geriatric, and nutritional assessments in older patients with diffuse large B-cell lymphoma: results of a multicentric prospective cohort study

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.

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.

Assessing Interobserver Variability in the Delineation of Structures in Radiation Oncology: A Systematic Review

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.

Quantitative MRI to Characterize Hypoxic Tumors in Comparison to FMISO PET/CT for Radiotherapy in Oropharynx Cancers

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.

Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning

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.

Impact of EGFRA289T/V mutation on relapse pattern in glioblastoma

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.

Evaluation of sarcopenia biomarkers in older patients undergoing major surgery for digestive cancer. SAXO prospective cohort study

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/m²). 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.

Multi-task multi-scale learning for outcome prediction in 3D PET images

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.

[What dose escalation in the treatment of locally advanced non-small cell lung cancer?]

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).

Impact of EGFRA289T/Vmutation on relapse pattern in glioblastoma

2046

Background: The prediction of the relapse pattern is an important issue in glioblastoma for personalized approach. Molecular factors, such as MGMT promoter methylation, influence relapse in- or out-field of the initial radiotherapy volume. Recently, a recurrent mutation located at position 289 of the extracellular domain of the epidermal growth factor receptor ( EGFRA289mut) has been associated with a more infiltrative phenotype in glioblastoma. The primary objective of the present study was to explore the impact of EGFRA289mut on the pattern of relapse after a chemo-radiotherapy based treatment of patients suffering from glioblastoma. Methods: An ancillary study from a monocentric 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 EGFRA289 status (mutated versus wild-type). Primary endpoint was the overlap score (varying from 0 to 1) between initial irradiated tumor volume (Vinit) and relapse volume (Vr). Vinit was the initial 95% isodose of the radiotherapy and Vr was delineated using the enhanced MRI T1-weighted part of the relapse tumor. Secondary endpoints explored the impact of other EGFR extracellular mutations, EGFR amplification and EGFRvIII on the relapse pattern, as well as the impact of EGFRA289mut on survival. EGFR alterations were identified using next-generation sequencing or droplet-digital PCR based methods on formalin-fixed paraffin-embedded samples collected at initial diagnosis. Results: One hundred patients were included: 11% of the population had EGFRA289mut glioblastoma (n = 11/100). EGFRA289mut glioblastomas had a relapse pattern more marginal compared to EGFRA289wt glioblastomas: a mean overlap score Vinit/Vr of 0.78 was observed in the EGFRA289mut group versus 0.94 in the EGFRA289wt group (p = 0.021). The proportion of EGFR A289mut glioblastomas in the outfield relapse (overlap score < 0.8) has a trend to be higher than in the infield relapse (overlap score > 0.8): 25% (n = 2/8) versus 9.8% (n = 9/92) for the EGFRA289wt population, p = 0.21. Neither EGFR amplification nor EGFRvIII did influence overlap score Vinit/Vr. In our population, EGFRA289mut did not influence survival. Conclusions: EGFR A289mut influences the relapse pattern in a population of patients suffering from glioblastoma. The role of EGFRA289mut as a decision-making biomarker for personalized radiotherapy should now be investigated in dedicated clinical trial. Clinical trial information: NCT02617745.

Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24, 436

Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat and Vincent Grégoire were not included as authors in the original publication [...]

Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00465-z.

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08538-4.

Comparison of Hypermetabolic and Hypoxic Volumes Delineated on [18F]FDG and [18F]Fluoromisonidazole PET/CT in Non-small-cell Lung Cancer Patients

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-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) and [¹⁸F]fluoromisonidazole ([¹⁸F]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 [¹⁸F]FDG and [¹⁸F]FMISO volumes defined by PET/CT in NSCLC patients included in a prospective study.

PROCEDURES

Thirty-four patients with non-resectable lung cancer underwent [¹⁸F]FDG and [¹⁸F]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 [¹⁸F]FDG PET/CT (metabolic volumes), and SUV > 1.4 on pre-RT [¹⁸F]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 [¹⁸F]FDG volumes were not statistically different (30.4 cc vs 22.2; P = 0.12). The mean pre-RT SUVmax [¹⁸F]FDG was higher than per-RT SUVmax (12.7 vs 6.5; P < 0.0001). The mean [¹⁸F]FMISO SUVmax and volumes were 2.7 and 1.37 cc, respectively. Volume-based analysis showed good overlap between [¹⁸F]FDG and [¹⁸F]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 [¹⁸F]FDG and [¹⁸F]FMISO uptake is low in NSCLC, making it possible to envisage different management strategies as the studies in progress show.

Prognostic value of low skeletal muscle mass in patient treated by exclusive curative radiochemotherapy for a NSCLC

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/m² (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/m² (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.

Targeting the Urotensin II/UT G Protein-Coupled Receptor to Counteract Angiogenesis and Mesenchymal Hypoxia/Necrosis in Glioblastoma

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^99mTc-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.

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.

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

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.

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

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.

Benefits of positron emission tomography scans for the evaluation of radiotherapy

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 (¹⁸F)-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. (¹⁸F)-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.

Prognostic value of sarcopenia in patients treated by Radiochemotherapy for locally advanced oesophageal cancer

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 (¹⁸FDG-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/h²) 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 cm²/m². 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 ¹⁸FDG-PET/CT acquisitions.

How to work together between nuclear medicine and radiotherapy departments?

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.

Development of Novel 111-In-Labelled DOTA Urotensin II Analogues for Targeting the UT Receptor Overexpressed in Solid Tumours

Overexpression of G protein-coupled receptors (GPCRs) in tumours is widely used to develop GPCR-targeting radioligands for solid tumour imaging in the context of diagnosis and even treatment. The human vasoactive neuropeptide urotensin II (hUII), which shares structural analogies with somatostatin, interacts with a single high affinity GPCR named UT. High expression of UT has been reported in several types of human solid tumours from lung, gut, prostate, or breast, suggesting that UT is a valuable novel target to design radiolabelled hUII analogues for cancer diagnosis. In this study, two original urotensinergic analogues were first conjugated to a DOTA chelator via an aminohexanoic acid (Ahx) hydrocarbon linker and then -hUII and DOTA-urantide, complexed to the radioactive metal indium isotope to successfully lead to radiolabelled DOTA-Ahx-hUII and DOTA-Ahx-urantide. The ¹¹¹In-DOTA-hUII in human plasma revealed that only 30% of the radioligand was degraded after a 3-h period. DOTA-hUII and DOTA-urantide exhibited similar binding affinities as native peptides and relayed calcium mobilization in HEK293 cells expressing recombinant human UT. DOTA-hUII, not DOTA-urantide, was able to promote UT internalization in UT-expressing HEK293 cells, thus indicating that radiolabelled ¹¹¹In-DOTA-hUII would allow sufficient retention of radioactivity within tumour cells or radiolabelled DOTA-urantide may lead to a persistent binding on UT at the plasma membrane. The potential of these radioligands as candidates to target UT was investigated in adenocarcinoma. We showed that hUII stimulated the migration and proliferation of both human lung A549 and colorectal DLD-1 adenocarcinoma cell lines endogenously expressing UT. In vivo intravenous injection of ¹¹¹In-DOTA-hUII in C57BL/6 mice revealed modest organ signals, with important retention in kidney. ¹¹¹In-DOTA-hUII or ¹¹¹In-DOTA-urantide were also injected in nude mice bearing heterotopic xenografts of lung A549 cells or colorectal DLD-1 cells both expressing UT. The observed significant renal uptake and low tumour/muscle ratio (around 2.5) suggest fast tracer clearance from the organism. Together, DOTA-hUII and DOTA-urantide were successfully radiolabelled with ¹¹¹Indium, the first one functioning as a UT agonist and the second one as a UT-biased ligand/antagonist. To allow tumour-specific targeting and prolong body distribution in preclinical models bearing some solid tumours, these radiolabelled urotensinergic analogues should be optimized for being used as potential molecular tools for diagnosis imaging or even treatment tools.

Radiotherapy boost in patients with hypoxic lesions identified by 18F-FMISO PET/CT in non-small-cell lung carcinoma: can we expect a better survival outcome without toxicity? [RTEP5 long-term follow-up]

PURPOSE

Chemoradiotherapy is the reference curative-intent treatment for nonresectable locally advanced non-small-cell lung carcinoma (NSCLC), with unsatisfactory survival, partially due to radiation resistance in hypoxic tissues. The objective was to update survival and toxicity at 3 years following radiotherapy boost to hypoxic tumours in NSCLC patients treated with curative-intent chemoradiotherapy.

METHODS

This was an open-label, nonrandomized, multicentre, phase II clinical trial. ¹⁸F-Fluoromisonidazole (¹⁸F-FMISO) PET/CT was used to determine the hypoxic profile of the patients. ¹⁸F-FMISO-positive patients and those without organ-at-risk constraints received a radiotherapy boost (70-84 Gy); the others received standard radiotherapy (66 Gy). Overall survival (OS), progression-free survival (PFS) and safety were assessed.

RESULTS

A total of 54 patients were evaluated. OS and PFS rates at 3 years were 48.5% and 28.8%, respectively. The median OS in the ¹⁸F-FMISO-positive patients was 25.8 months and was not reached in the ¹⁸F-FMISO-negative patients (p = 0.01). A difference between the groups was also observed for PFS (12 months vs. 26.2 months, p = 0.048). In ¹⁸F-FMISO-positive patients, no difference was observed in OS in relation to dose, probably because of the small sample size (p = 0.30). However, the median OS seemed to be in favour of patients who received the radiotherapy boost (26.5 vs. 15.3 months, p = 0.71). In patients who received the radiotherapy boost, no significant late toxicities were observed.

CONCLUSION

¹⁸F-FMISO uptake in NSCLC patients is strongly associated with features indicating a poor prognosis. In ¹⁸F-FMISO-positive patients, the radiotherapy boost seemed to improve the OS by 11.2 months. A further clinical trial is needed to investigate the efficacy of a radiotherapy boost in patients with hypoxic tumours.

Lower Subcutaneous or Visceral Adiposity Assessed by Abdominal Computed Tomography Could Predict Adverse Outcome in Patients With Crohn's Disease

BACKGROUND AND AIMS

Changes in body composition have been described in patients with Crohn's disease, but their predictive performances on disease evolution remain undefined. The aims of our study were to assess, in patients with Crohn's disease requiring abdominal computed tomography, body composition by computed tomography, and to study the outcome according to various body composition parameters at the time of the computed tomography.

METHODS

Patients with Crohn's disease who underwent abdominal computed tomography for suspected complications were retrospectively included. The definition of adverse outcome included death or need for surgery within 6 months of the computed tomography. Skeletal muscle index and visceral and subcutaneous adiposity indexes were calculated from tissue surface areas measured at the third lumbar vertebra, divided by the height squared.

RESULTS

The prevalence of underweight was 26.8% and the prevalence of sarcopenia was 33.6%. After gender adjustment, skeletal muscle index tended to be reduced in patients with adverse outcome, compared with patients without surgery or death [p = 0.07]. Moreover, subcutaneous adiposity index and visceral adiposity index were significantly lower in patients with surgery or death [p = 0.009 and p < 0.001, respectively]. These differences were almost equivalent in both genders for the subcutaneous adiposity index but were clearly stronger in men for the visceral adiposity index.

CONCLUSIONS

Subcutaneous and visceral adiposity indexes correlate inversely with adverse outcome in patients with Crohn's disease. Alteration of body composition assessed by computed tomography in these patients appears to be a marker of disease severity.

FDG and FMISO PET-guided dose escalation with intensity-modulated radiotherapy in lung cancer

Background

Concomitant chemo-radiotherapy is the reference treatment for non-resectable locally-advanced Non-Small Cell Lung Cancer (NSCLC). Increasing radiotherapy total dose in the whole tumour volume has been shown to be deleterious. Functional imaging with positron emission tomography (PET/CT) offers the potential to identify smaller and biologically meaningful target volumes that could be irradiated with larger doses without compromising Organs At Risk (OAR) tolerance. This study investigated four scenarios, based on ¹⁸FDG and ¹⁸F-miso PET/CT, to delineate the target volumes and derive radiotherapy plans delivering up to 74Gy.

Method

Twenty-one NSCLC patients, selected from a prospective phase II trial, had ¹⁸FDG- and ¹⁸F-miso PET/CT before the start of radiotherapy and ¹⁸FDG PET/CT during the radiotherapy (42Gy). The plans were based planned on a standard plan delivering 66 Gy (plan 1) and on three different boost strategies to deliver 74Gy total dose in pre-treatment ¹⁸FDG hotspot (70% of SUV_max) (plan 2), pre-treatment ¹⁸F-miso target (SUV_max > 1.4) (plan 3) and per-treatment ¹⁸FDG residual (40% of SUV_max). (plan 4).

Results

The mean target volumes were 4.8 cc (± 1.1) for ¹⁸FDG hotspot, 38.9 cc (± 14.5) for ¹⁸F-miso and 36.0 cc (± 10.1) for per-treatment ¹⁸FDG. In standard plan (66 Gy), the mean dose covering 95% of the PTV (D95%) were 66.5 (± 0.33), 66.1 (± 0.32) and 66.1 (± 0.32) Gy for ¹⁸FDG hotspot, ¹⁸F-miso and per-treatment ¹⁸FDG. In scenario 2, the mean D95% was 72.5 (± 0.25) Gy in ¹⁸FDG hotspot versus 67.9 (± 0.49) and 67.9 Gy (± 0.52) in ¹⁸F-miso and per-treatment ¹⁸FDG, respectively. In scenario 3, the mean D95% was 72.2 (± 0.27) Gy to ¹⁸F-miso versus 70.4 (± 0.74) and 69.5Gy (± 0.74) for ¹⁸FDG hotspot and per-treatment ¹⁸FDG, respectively. In scenario 4, the mean D95% was 73.1 (± 0.3) Gy to ¹⁸FDG per-treatment versus 71.9 (± 0.61) and 69.8 (± 0.61) Gy for ¹⁸FDG hotspot and ¹⁸F-miso, respectively. The dose/volume constraints to OARs were matched in all scenarios.

Conclusion

Escalated doses can be selectively planned in NSCLC target volumes delineated on ¹⁸FDG and ¹⁸F-miso PET/CT functional images. The most relevant strategy should be investigated in clinical trials.

Trial registration

(RTEP5, NCT01576796, registered 15 june 2012)

Spotting L3 slice in CT scans using deep convolutional network and transfer learning

In this article, we present a complete automated system for spotting a particular slice in a complete 3D Computed Tomography exam (CT scan). Our approach does not require any assumptions on which part of the patient's body is covered by the scan. It relies on an original machine learning regression approach. Our models are learned using the transfer learning trick by exploiting deep architectures that have been pre-trained on imageNet database, and therefore it requires very little annotation for its training. The whole pipeline consists of three steps: i) conversion of the CT scans into Maximum Intensity Projection (MIP) images, ii) prediction from a Convolutional Neural Network (CNN) applied in a sliding window fashion over the MIP image, and iii) robust analysis of the prediction sequence to predict the height of the desired slice within the whole CT scan. Our approach is applied to the detection of the third lumbar vertebra (L3) slice that has been found to be representative to the whole body composition. Our system is evaluated on a database collected in our clinical center, containing 642 CT scans from different patients. We obtained an average localization error of 1.91±2.69 slices (less than 5 mm) in an average time of less than 2.5 s/CT scan, allowing integration of the proposed system into daily clinical routines.

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier

Purpose

In oncology, texture features extracted from positron emission tomography with 18-fluorodeoxyglucose images (FDG-PET) are of increasing interest for predictive and prognostic studies, leading to several tens of features per tumor. To select the best features, the use of a random forest (RF) classifier was investigated.

Methods

Sixty-five patients with an esophageal cancer treated with a combined chemo-radiation therapy were retrospectively included. All patients underwent a pretreatment whole-body FDG-PET. The patients were followed for 3 years after the end of the treatment. The response assessment was performed 1 month after the end of the therapy. Patients were classified as complete responders and non-complete responders. Sixty-one features were extracted from medical records and PET images. First, Spearman’s analysis was performed to eliminate correlated features. Then, the best predictive and prognostic subsets of features were selected using a RF algorithm. These results were compared to those obtained by a Mann-Whitney U test (predictive study) and a univariate Kaplan-Meier analysis (prognostic study).

Results

Among the 61 initial features, 28 were not correlated. From these 28 features, the best subset of complementary features found using the RF classifier to predict response was composed of 2 features: metabolic tumor volume (MTV) and homogeneity from the co-occurrence matrix. The corresponding predictive value (AUC = 0.836 ± 0.105, Se = 82 ± 9%, Sp = 91 ± 12%) was higher than the best predictive results found using the Mann-Whitney test: busyness from the gray level difference matrix (P < 0.0001, AUC = 0.810, Se = 66%, Sp = 88%). The best prognostic subset found using RF was composed of 3 features: MTV and 2 clinical features (WHO status and nutritional risk index) (AUC = 0.822 ± 0.059, Se = 79 ± 9%, Sp = 95 ± 6%), while no feature was significantly prognostic according to the Kaplan-Meier analysis.

Conclusions

The RF classifier can improve predictive and prognostic values compared to the Mann-Whitney U test and the univariate Kaplan-Meier survival analysis when applied to several tens of features in a limited patient database.

Correlation between fluorodeoxyglucose hotspots on pretreatment positron emission tomography/CT and preferential sites of local relapse after chemoradiotherapy for head and neck squamous cell carcinoma

BACKGROUND

The potential benefits of ¹⁸ F-fluoro-2-deoxy-D-glucose-positron emission tomography/CT (FDG-PET/CT) imaging for radiotherapy (RT) treatment planning of head and neck squamous cell carcinoma (HNSCC) are increasingly being recognized. It has been suggested that intratumoral subvolumes with high FDG avidity ("hotspots") are potential targets for selected dose escalation. The purposes of this study were to demonstrate that pre-RT FDG-PET/CT can identify intratumoral sites at increased risk of local relapse after RT and to determine an optimal threshold to delineate smaller RT target volumes that would facilitate RT dose escalation without impaired tolerance.

METHODS

Seventy-two consecutive patients with locally advanced HNSCC treated by RT ± chemotherapy were included in this study. All patients underwent FDG-PET/CT at initial staging (PET_A ) and during systematic follow-up (PET_R ). FDG-PET/CT was coregistered on the initial CT scan with a rigid method. Various subvolumes (A_X ; × = 30%, 40%, 50%, 60%, 70%, 80%, and 90% standardized uptake value maximum [SUVmax] thresholds) within the primary tumor and in the subsequent local relapse (R_X ; × = 40% and 70% SUVmax thresholds) were compared together (Dice, Jaccard, overlap fraction, common volume/baseline volume, and common volume/recurrent volume).

RESULTS

Nineteen patients (26%) had local relapses. Using a 40% SUVmax threshold, the initial metabolic tumor volume was significantly higher in patients with local relapses than in controlled patients (10.4 ± 8.6 vs 5.1 ± 4.9 cc; p = .002) as well as total lesion glycolysis (117.9 ± 88.6 vs 60.6 ± 80.4; p = .013). For both methods, the overlap index among A₃₀ , A₄₀ , and A₅₀ subvolumes on PET_A and the whole metabolic volume of recurrence R₄₀ and R₇₀ on PET_R showed a moderate agreement (0.52 to 0.43).

CONCLUSION

Our study does not find high overlap index values between the initial tumor and recurrence subvolumes, probably because of a suboptimal coregistration. Our results also confirm that metabolic tumor volume and total lesion glycolysis are independently correlated with recurrence-free survival in patients with HNSCC. Further larger prospective studies with FDG-PET/CT performed in the same RT position and with a validated elastic registration method are needed. © 2017 Wiley Periodicals, Inc. Head Neck 39: 1155-1165, 2017.

Phase II Study of a Radiotherapy Total Dose Increase in Hypoxic Lesions Identified by 18F-Misonidazole PET/CT in Patients with Non-Small Cell Lung Carcinoma (RTEP5 Study)

See an invited perspective on this article on page 1043.This multicenter phase II study investigated a selective radiotherapy dose increase to tumor areas with significant ¹⁸F-misonidazole (¹⁸F-FMISO) uptake in patients with non-small cell lung carcinoma (NSCLC). Methods: Eligible patients had locally advanced NSCLC and no contraindication to concomitant chemoradiotherapy. The ¹⁸F-FMISO uptake on PET/CT was assessed by trained experts. If there was no uptake, 66 Gy were delivered. In ¹⁸F-FMISO-positive patients, the contours of the hypoxic area were transferred to the radiation oncologist. It was necessary for the radiotherapy dose to be as high as possible while fulfilling dose-limiting constraints for the spinal cord and lungs. The primary endpoint was tumor response (complete response plus partial response) at 3 mo. The secondary endpoints were toxicity, disease-free survival (DFS), and overall survival at 1 y. The target sample size was set to demonstrate a response rate of 40% or more (bilateral α = 0.05, power 1-β = 0.95). Results: Seventy-nine patients were preincluded, 54 were included, and 34 were ¹⁸F-FMISO-positive, 24 of whom received escalated doses of up to 86 Gy. The response rate at 3 mo was 31 of 54 (57%; 95% confidence interval [CI], 43%-71%) using RECIST 1.1 (17/34 responders in the ¹⁸F-FMISO-positive group). DFS and overall survival at 1 y were 0.86 (95% CI, 0.77-0.96) and 0.63 (95% CI, 0.49-0.74), respectively. DFS was longer in the ¹⁸F-FMISO-negative patients (P = 0.004). The radiotherapy dose was not associated with DFS when adjusting for the ¹⁸F-FMISO status. One toxic death (66 Gy) and 1 case of grade 4 pneumonitis (>66 Gy) were reported. Conclusion: Our approach results in a response rate of 40% or more, with acceptable toxicity. ¹⁸F-FMISO uptake in NSCLC patients is strongly associated with poor prognosis features that could not be reversed by radiotherapy doses up to 86 Gy.

SPECT-computed tomography in rats with TNBS-induced colitis: A first step toward functional imaging

AIM

To assess the feasibility of SPECT-computed tomography (CT) in rats with trinitrobenzene sulfonic acid (TNBS)-induced acute colitis and confront it with model inflammatory characteristics.

METHODS

Colitis was induced in Sprague-Dawley rats by intrarectal injection of TNBS (n = 10) while controls received vehicle (n = 10). SPECT-CT with intravenous injection of 10 MBq of 67Ga-Citrate was performed at day 2. SPECT-CT criteria were colon wall thickness and maximal wall signal intensity. Laboratory parameters were assessed: colon weight:length ratio, colon cyclooxygenase-2 expression by western blot and histological inflammatory score.

RESULTS

Colon weight/length ratio, colon COX-2 expression and histological inflammatory score were significantly higher in the TNBS group than in the control group (P = 0.0296, P < 0.0001, P = 0.0007 respectively). Pixel max tend to be higher in the TNBS group than in the control group but did not reach statistical significance (P = 0.0662). Maximal thickness is significantly increased in the TNBS group compared to the control group (P = 0.0016) while colon diameter is not (P = 0.1904). Maximal thickness and colon diameter were correlated to colon COX-2 expression (P = 0.0093, P = 0.009 respectively) while pixel max was not (P = 0.22). Maximal thickness was significantly increased when inflammation was histologically observed (P = 0.0043) while pixel max and colon diameter did not (P = 0.2452, P = 0.3541, respectively).

CONCLUSION

SPECT-CT is feasible and easily distinguished control from colitic rats.

Baseline Total Metabolic Tumor Volume Measured with Fixed or Different Adaptive Thresholding Methods Equally Predicts Outcome in Peripheral T Cell Lymphoma

The purpose of this study was to compare in a large series of peripheral T cell lymphoma, as a model of diffuse disease, the prognostic value of baseline total metabolic tumor volume (TMTV) measured on ¹⁸F-FDG PET/CT with adaptive thresholding methods with TMTV measured with a fixed 41% SUV_max threshold method.

METHODS

One hundred six patients with peripheral T cell lymphoma, staged with PET/CT, were enrolled from 5 Lymphoma Study Association centers. In this series, TMTV computed with the 41% SUV_max threshold is a strong predictor of outcome. On a dedicated workstation, we measured the TMTV with 4 adaptive thresholding methods based on characteristic image parameters: Daisne (Da) modified, based on signal-to-background ratio; Nestle (Ns), based on tumor and background intensities; Fit, including a 3-dimensional geometric model based on spatial resolution (Fit); and Black (Bl), based on mean SUV_max The TMTV values obtained with each adaptive method were compared with those obtained with the 41% SUV_max method. Their respective prognostic impacts on outcome prediction were compared using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves.

RESULTS

The median value of TMTV_41%, TMTV_Da, TMTV_Ns, TMTV_Fit, and TMTV_Bl were, respectively, 231 cm³ (range, 5-3,824), 175 cm³ (range, 8-3,510), 198 cm³ (range, 3-3,934), 175 cm³ (range, 8-3,512), and 333 cm³ (range, 3-5,113). The intraclass correlation coefficients were excellent, from 0.972 to 0.988, for TMTV_Da, TMTV_Fit, and TMTV_Ns, and less good for TMTV_Bl (0.856). The mean differences obtained from the Bland-Altman plots were 48.5, 47.2, 19.5, and -253.3 cm³, respectively. Except for Black, there was no significant difference within the methods between the ROC curves (P > 0.4) for progression-free survival and overall survival. Survival curves with the ROC optimal cutoff for each method separated the same groups of low-risk (volume ≤ cutoff) from high-risk patients (volume > cutoff), with similar 2-y progression-free survival (range, 66%-72% vs. 26%-29%; hazard ratio, 3.7-4.1) and 2-y overall survival (79%-83% vs. 50%-53%; hazard ratio, 3.0-3.5).

CONCLUSION

The prognostic value of TMTV remained quite similar whatever the methods, adaptive or 41% SUV_max, supporting its use as a strong prognosticator in lymphoma. However, for implementation of TMTV in clinical trials 1 single method easily applicable in a multicentric PET review must be selected and kept all along the trial.

High FDG uptake areas on pre-radiotherapy PET/CT identify preferential sites of local relapse after chemoradiotherapy for locally advanced oesophageal cancer

PURPOSE

The high failure rates in the radiotherapy (RT) target volume suggest that patients with locally advanced oesophageal cancer (LAOC) would benefit from increased total RT doses. High 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG) uptake (hotspot) on pre-RT FDG positron emission tomography (PET)/CT has been reported to identify intra-tumour sites at increased risk of relapse after RT in non-small cell lung cancer and in rectal cancer. Our aim was to confirm these observations in patients with LAOC and to determine the optimal maximum standardized uptake value (SUVmax) threshold to delineate smaller RT target volumes that would facilitate RT dose escalation without impaired tolerance.

METHODS

The study included 98 consecutive patients with LAOC treated by chemoradiotherapy (CRT). All patients underwent FDG PET/CT at initial staging and during systematic follow-up in a single institution. FDG PET/CT acquisitions were coregistered on the initial CT scan. Various subvolumes within the initial tumour (30, 40, 50, 60, 70, 80 and 90% SUVmax thresholds) and in the subsequent local recurrence (LR, 40 and 90% SUVmax thresholds) were pasted on the initial CT scan and compared[Dice, Jaccard, overlap fraction (OF), common volume/baseline volume, common volume/recurrent volume].

RESULTS

Thirty-five patients had LR. The initial metabolic tumour volume was significantly higher in LR tumours than in the locally controlled tumours (mean 25.4 vs 14.2 cc; p = 0.002). The subvolumes delineated on initial PET/CT with a 30-60% SUVmax threshold were in good agreement with the recurrent volume at 40% SUVmax (OF = 0.60-0.80). The subvolumes delineated on initial PET/CT with a 30-60% SUVmax threshold were in good to excellent agreement with the core volume (90% SUVmax) of the relapse (common volume/recurrent volume and OF indices 0.61-0.89).

CONCLUSION

High FDG uptake on pretreatment PET/CT identifies tumour subvolumes that are at greater risk of recurrence after CRT in patients with LAOC. We propose a 60% SUVmax threshold to delineate high FDG uptake areas on initial PET/CT as reduced target volumes for RT dose escalation.

Areas of high 18F-FDG uptake on preradiotherapy PET/CT identify preferential sites of local relapse after chemoradiotherapy for non-small cell lung cancer

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. Areas of high (18)F-FDG uptake on preradiotherapy (18)F-FDG PET/CT have been reported to identify intratumor subvolumes at high risk of relapse after radiotherapy. We wanted to confirm these observations on a cohort of patients included in 3 sequential prospective studies. Our aim was to assess an appropriate threshold (percentage of maximum standardized uptake value [SUVmax]) to delineate subvolumes on staging (18)F-FDG PET/CT scans assuming that a smaller target volume would facilitate isotoxic radiotherapy dose escalation.

METHODS

Thirty-nine patients with inoperable stage II or III NSCLC, treated with chemoradiation or with radiotherapy alone, were extracted from 3 prospective studies (ClinicalTrials.gov identifiers NCT01261585, NCT01261598, and RECF0645). All patients underwent (18)F-FDG PET/CT at initial staging, before radiotherapy, during radiotherapy, and during systematic follow-up in a single institution. All (18)F-FDG PET/CT acquisitions were coregistered on the initial scan. Various subvolumes in the initial acquisition (30%, 40%, 50%, 60%, 70%, 80%, and 90% SUVmax thresholds) and in the 3 subsequent acquisitions (40% and 90% SUVmax thresholds) were pasted on the initial scan and compared.

RESULTS

Seventeen patients had a local relapse. The SUVmax measured during radiotherapy was significantly higher in locally relapsed tumors than in locally controlled tumors (mean, 6.8 vs. 4.6; P = 0.02). The subvolumes delineated on initial PET/CT scans with 70%-90% SUVmax thresholds were in good agreement with the recurrent volume at a 40% SUVmax threshold (common volume/baseline volume, 0.60-0.80). The subvolumes delineated on initial PET/CT scans with 30%-60% SUVmax thresholds were in good to excellent agreement with the core volume of the relapse (90% SUVmax threshold) (common volume/recurrent volume and overlap fraction indices, 0.60-0.93). The agreement was moderate (>0.51) when a 70% SUVmax threshold was used to delineate on initial PET/CT scans.

CONCLUSION

High (18)F-FDG uptake areas on pretreatment PET/CT scans identify tumor subvolumes at greater risk of relapse in patients with NSCLC treated by concomitant chemoradiation. We propose a 70% SUVmax threshold to delineate areas of high (18)F-FDG uptake on initial PET/CT scans as the target volumes for potential radiotherapy dose escalation.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.