RESUMEN
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 [18F]fluorodeoxyglucose ([18F]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 [18F]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/m2 intravenously once every 3 weeks and carboplatin area under the curve [AUC]=6 once every 3 weeks, or cisplatin 80 mg/m2 intravenously once every 3 weeks and vinorelbine 30 mg/m2 intravenously on day 1 and 60 mg/m2 orally [or 30 mg/m2 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/m2 intravenously and carboplatin AUC=2 or cisplatin 80 mg/m2 intravenously and vinorelbine 20 mg/m2 intravenously on day 1 and 40 mg/m2 orally (or 20 mg/m2 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 [18F]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 [18F]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 [18F]FDG-PET-guided radiotherapy dose adaptation in patients with stage III NSCLC. FUNDING: Programme Hospitalier de Recherche Clinique National 2014.
RESUMEN
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.
Asunto(s)
Linfoma de Células B Grandes Difuso , Sarcopenia , Humanos , Anciano , Pronóstico , Evaluación Nutricional , Estudios Prospectivos , Equilibrio Postural , Estudios Retrospectivos , Estudios de Tiempo y Movimiento , Linfoma de Células B Grandes Difuso/tratamiento farmacológicoRESUMEN
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.
Asunto(s)
Neoplasias de la Mama , Aprendizaje Profundo , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/patología , Medios de Contraste , Femenino , Humanos , Mamografía/métodos , Receptores de Estrógenos , Estudios RetrospectivosRESUMEN
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 [...].
RESUMEN
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.
RESUMEN
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.
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/terapia , Quimioradioterapia/métodos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/terapia , Radioterapia/métodos , Anciano , Carcinoma de Pulmón de Células no Pequeñas/mortalidad , Femenino , Estudios de Seguimiento , Francia , Humanos , Hipoxia , Neoplasias Pulmonares/mortalidad , Masculino , Persona de Mediana Edad , Misonidazol/análogos & derivados , Seguridad del Paciente , Tomografía Computarizada por Tomografía de Emisión de Positrones , Supervivencia sin Progresión , Estudios Prospectivos , Radiofármacos/uso terapéutico , Resultado del TratamientoRESUMEN
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.
Asunto(s)
Neoplasias Esofágicas/diagnóstico por imagen , Fluorodesoxiglucosa F18 , Recurrencia Local de Neoplasia/diagnóstico por imagen , Tomografía de Emisión de Positrones , Radiofármacos , Anciano , Quimioradioterapia , Neoplasias Esofágicas/terapia , Femenino , Humanos , Masculino , Persona de Mediana Edad , Imagen Multimodal , Recurrencia Local de Neoplasia/terapia , Tomografía Computarizada por Rayos XRESUMEN
PURPOSE: It has been suggested that FDG PET has predictive value for the prognosis of treated oesophageal carcinoma. However, the studies reported in the literature have shown discordant results. The aim of this study was to determine whether pretherapy quantitative metabolic parameters correlate with patient outcomes. METHODS: Included in the study were 67 patients with a histological diagnosis of oesophageal squamous cell carcinoma. Each patient underwent (18)F-FDG PET (4.5 MBq/kg) before chemoradiotherapy. Quantitative analysis was performed using the following parameters: age, weight loss, location, N stage, OMS performance status, MTVp and MTVp' (metabolic tumour volume determined by two different physicians), MTV40% (volume for a threshold of 40 % of SUVmax), MTVa (volume automatically determined with a contrast-based adaptive threshold method), SUVmax, SUVmean and TLG (total lesion glycolysis). RESULTS: MTVp and MTV40% were highly correlated (Pearson's index 0.92). SUVmeanp and SUVmean40% were also correlated (Pearson's index 0.86), as were TLGp and TLG40% (Pearson's index 0.98). Similarly, the parameters obtained with the adaptive threshold method (MTVa, SUVmeana and TLGa) were correlated with those obtained manually (MTVp, SUVmeanp and TLGp). The manual metabolic tumour volume determination (MTVp and MTVp') was reproducible. Multivariate analysis for disease-free survival (DFS) showed that a larger MTVp was associated with a shorter DFS (p = 0.004) and that a higher SUVmax was associated with a longer DFS (p = 0.02). Multivariate analysis for overall survival (OS) showed that a larger MTVp was associated with a shorter OS (p = 0.01) and that a tumour in the distal oesophagus was associated with a longer OS (p = 0.005). The associations among the other parameters were not statistically significant. CONCLUSION: Metabolic tumour volume is a major prognostic factor for DFS and OS in patients with oesophageal squamous cell carcinoma. Higher SUVmax values were paradoxically associated with longer survival. The location of the tumour also appeared to affect prognosis.
Asunto(s)
Carcinoma de Células Escamosas/metabolismo , Carcinoma de Células Escamosas/patología , Neoplasias Esofágicas/metabolismo , Neoplasias Esofágicas/patología , Carga Tumoral , Adulto , Anciano , Anciano de 80 o más Años , Carcinoma de Células Escamosas/diagnóstico por imagen , Carcinoma de Células Escamosas/terapia , Quimioradioterapia , Supervivencia sin Enfermedad , Neoplasias Esofágicas/diagnóstico por imagen , Neoplasias Esofágicas/terapia , Carcinoma de Células Escamosas de Esófago , Femenino , Fluorodesoxiglucosa F18 , Humanos , Masculino , Persona de Mediana Edad , Tomografía de Emisión de Positrones , Estudios RetrospectivosRESUMEN
PURPOSE: To assess prospectively the prognostic value of FDG PET/CT during curative-intent radiotherapy (RT) with or without concomitant chemotherapy in patients with non-small-cell lung cancer (NSCLC). METHODS: Patients with histological proof of invasive localized NSCLC and evaluable tumour, and who were candidates for curative-intent radiochemotherapy (RCT) or RT were preincluded after providing written informed consent. Definitive inclusion was conditional upon significant FDG uptake before RT (PET1). All included patients had a FDG PET/CT scan during RT (PET2, mean dose 43 Gy) and were evaluated by FDG PET/CT at 3 months and 1 year after RT. The main endpoint was death (from whatever cause) or tumour progression at 1 year. RESULTS: Of 77 patients preincluded, 52 were evaluable. Among the evaluable patients, 77% received RT with induction chemotherapy and 73% RT with concomitant chemotherapy. At 1 year, 40 patients (77 %) had died or had tumour progression. No statistically significant association was found between stage (IIIB vs. other), histology (squamous cell carcinoma vs. other), induction or concomitant chemotherapy, and death/tumour progression at 1 year. The SUVmax in the PET2 scan was the single variable predictive of death or tumour progression at 1 year (odds ratio 1.97, 95% CI 1.25 - 3.09, p = 0.003) in multivariate analysis. The area under the receiver operating characteristic curve was 0.85 (95% CI 0.73 - 0.94, p < 10(-4)). A SUVmax value of 5.3 in the PET2 scan yielded a sensitivity of 70% and a specificity of 92% for predicting tumour progression or death at 1 year. CONCLUSION: This prospective multicentre study demonstrated the prognostic value in terms of disease-free survival of SUVmax assessed during the 5th week of curative-intent RT or RCT in NSCLC patients (NCT01261598; RTEP2 study).
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Quimioradioterapia , Fluorodesoxiglucosa F18 , Neoplasias Pulmonares/diagnóstico por imagen , Tomografía de Emisión de Positrones , Radiofármacos , Anciano , Carcinoma de Pulmón de Células no Pequeñas/terapia , Supervivencia sin Enfermedad , Femenino , Humanos , Neoplasias Pulmonares/terapia , Masculino , Persona de Mediana Edad , Imagen Multimodal , Valor Predictivo de las Pruebas , Estudios Prospectivos , Tomografía Computarizada por Rayos X , Resultado del TratamientoRESUMEN
OBJECTIVES: Approximately 30% of DLBCL patients are older than 70 yr. This study evaluated the prognostic impact of a cachexia score (CS) including fat tissue loss (adipopenia) and sarcopenia as assessed by computed tomography (CT scan) in elderly DLBCL patients treated with chemotherapy and rituximab (R). METHODS: This retrospective analysis included 80 DLBCL patients older than 70 yr treated with R-CHOP or R-miniCHOP. Skeletal muscle (SM) and visceral (V) and subcutaneous (S) adipose (A) tissues were measured by analysing CT images at the third lumbar (L3) level. RESULTS: The median age of the patients was 78 yr. Forty-four and 46 patients were considered sarcopenic and adipopenic, respectively. The median progression-free survival (PFS) was 13.6 months in the adipopenic group and 49.4 months in the non-adipopenic group [hazard ratio (HR) = 2.27; 95% confidence interval (CI): 1.3-4; P = 0.0042]. The median overall survival (OS) was 25.7 months in the adipopenic group and 57.1 months in the non-adipopenic group (HR = 1.93; 95% CI: 1.05-3.55; P = 0.0342). A two-point CS including adipopenia and sarcopenia was created and defined two distinct risk groups with differences in outcomes that were highly significant. The CS was predictive of the prognosis in a multivariate analysis including body mass index (BMI) (< or ≥ 25 kg/m(2) ), age (< or ≥ 80 yr), international prognostic index (IPI) and albuminaemia (HR = 3.67; 95% CI = 1.93-6.97; P < 0.0001). CONCLUSION: A CS including sarcopenia and adipopenia, assessed by a single CT scan slice, predicts outcome independent of BMI and the IPI.
Asunto(s)
Tejido Adiposo/patología , Caquexia/complicaciones , Linfoma de Células B Grandes Difuso/terapia , Anciano , Anciano de 80 o más Años , Terapia Combinada , Femenino , Humanos , Inmunoterapia , Linfoma de Células B Grandes Difuso/complicaciones , Linfoma de Células B Grandes Difuso/tratamiento farmacológico , Masculino , Pronóstico , Estudios Retrospectivos , Tomografía Computarizada por Rayos XRESUMEN
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 (HD95), 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 HD95 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.
Asunto(s)
Neoplasias de Cabeza y Cuello , Imagen por Resonancia Magnética , Órganos en Riesgo , Planificación de la Radioterapia Asistida por Computador , Tomografía Computarizada por Rayos X , Humanos , Tomografía Computarizada por Rayos X/métodos , Imagen por Resonancia Magnética/métodos , Neoplasias de Cabeza y Cuello/diagnóstico por imagen , Neoplasias de Cabeza y Cuello/radioterapia , Órganos en Riesgo/efectos de la radiación , Planificación de la Radioterapia Asistida por Computador/métodosRESUMEN
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.
Asunto(s)
Oncología por Radiación , Humanos , Variaciones Dependientes del Observador , Reproducibilidad de los Resultados , Planificación de la Radioterapia Asistida por Computador/métodosRESUMEN
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.
Asunto(s)
Neoplasias Encefálicas , Recurrencia Local de Neoplasia , Humanos , Neoplasias Encefálicas/diagnóstico por imagen , Encéfalo , Procesamiento de Imagen Asistido por ComputadorRESUMEN
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 ClavienâDindo 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 IMATBMI (0.35 vs. 0.22; p = 0.003) and increased VATBMI (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 IMATBMI (0.31 vs. 0.23; p = 0.010) and VATBMI (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.
Asunto(s)
Neoplasias Gastrointestinales , Sarcopenia , Humanos , Anciano , Sarcopenia/diagnóstico , Sarcopenia/diagnóstico por imagen , Fuerza de la Mano , Estudios Prospectivos , Músculo Esquelético/diagnóstico por imagen , Obesidad/complicaciones , BiomarcadoresRESUMEN
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.
RESUMEN
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.
Asunto(s)
Neoplasias Hematológicas , Sarcopenia , Sepsis , Choque Séptico , Humanos , Choque Séptico/complicaciones , Choque Séptico/epidemiología , Sarcopenia/complicaciones , Sarcopenia/epidemiología , Enfermedad Crítica , Estudios Retrospectivos , Prevalencia , Sepsis/complicaciones , Sepsis/epidemiología , Neoplasias Hematológicas/complicaciones , Unidades de Cuidados IntensivosRESUMEN
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.
Asunto(s)
Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/patología , Imagenología Tridimensional , Curva ROC , Tomografía de Emisión de Positrones/métodosRESUMEN
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.
RESUMEN
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 SUVmax 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 SUVmax 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.