Technical note: Impact of dose voxel kernel (DVK) values on dosimetry estimates in 177 Lu and 90 Y radiopharmaceutical therapy (RPT) applications

BACKGROUND

Radiopharmaceutical therapy (RPT) is an increasingly adopted modality for treating cancer. There is evidence that the optimization of the treatment based on dosimetry can improve outcomes. However, standardization of the clinical dosimetry workflow still represents a major effort. Among the many sources of variability, the impact of using different Dose Voxel Kernels (DVKs) to generate absorbed dose (AD) maps by convolution with the time-integrated activity (TIA) distribution has not been systematically investigated.

PURPOSE

This study aims to compare DVKs and assess the differences in the ADs when convolving the same TIA map with different DVKs.

METHODS

DVKs of 3 × 3 × 3 mm3 sampling-nine for 177 Lu, nine for 90 Y-were selected from those most used in commercial/free software or presented in prior publications. For each voxel within a 11 × 11 × 11 matrix, the coefficient of variation (CoV) and the percentage difference between maximum and minimum values (% maximum difference) were calculated. The total absorbed dose per decay (SUM), calculated as the sum of all the voxel values in each kernel, was also compared. Publicly available quantitative SPECT images for two patients treated with 177 Lu-DOTATATE and PET images for two patients treated with 90 Y-microspheres were used, including organs at risk (177 Lu: kidneys; 90 Y: liver and healthy liver) and tumors' segmentations. For each patient, the mean AD to the volumes of interest (VOIs) was calculated using the different DVKs, the same TIA map and the same software tool for dose convolution, thereby focusing on the DVK impact. For each VOI, the % maximum difference of the mean AD between maximum and minimum values was computed.

RESULTS

The CoV (% maximum difference) in voxels of normalized coordinates [0,0,0], [0,1,0], and [0,1,1] were 5%(21%), 9%(35%), and 10%(46%) for the 177 Lu DVKs. For the case of 90 Y, these values were 2%(9%), 4%(14%), and 4%(16%). The CoV (% maximum difference) for SUM was 9%(33%) for 177 Lu, and 4%(15%) for 90 Y. The variability of the mean tumor and organ AD was up to 19% and 15% in 177 Lu-DOTATATE and 90 Y-microspheres patients, respectively.

CONCLUSIONS

This study showed a considerable AD variability due exclusively to the use of different DVKs. A concerted effort by the scientific community would contribute to decrease these discrepancies, strengthening the consistency of AD calculation in RPT.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

OBJECTIVE

The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome.

METHODS

A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer.

RESULTS

The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set).

CONCLUSIONS

The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

[18 F]-FDG PET radiomic model as prognostic biomarker in diffuse large B-cell lymphoma

To evaluate the association between radiomic features (RFs) extracted from 18 F-FDG PET/CT (18 F-FDG-PET) with progression-free survival (PFS) and overall survival (OS) in diffuse large-B-cell lymphoma (DLBCL) patients eligible to first-line chemotherapy. DLBCL patients who underwent 18 F-FDG-PET prior to first-line chemotherapy were retrospectively analyzed. RFs were extracted from the lesion showing the highest uptake. A radiomic score to predict PFS and OS was obtained by multivariable Elastic Net Cox model. Radiomic univariate model, clinical and combined clinical-radiomic multivariable models to predict PFS and OS were obtained. 112 patients were analyzed. Median follow-up was 34.7 months (Inter-Quartile Range (IQR) 11.3-66.3 months) for PFS and 41.1 (IQR 18.4-68.9) for OS. Radiomic score resulted associated with PFS and OS (p < 0.001), outperforming conventional PET parameters. C-index (95% CI) for PFS prediction were 0.67 (0.58-0.76), 0.81 (0.75-0.88) and 0.84 (0.77-0.91) for clinical, radiomic and combined clinical-radiomic model, respectively. C-index for OS were 0.77 (0.66-0.89), 0.84 (0.76-0.91) and 0.90 (0.81-0.98). In the Kaplan-Meier analysis (low-IPI vs. high-IPI), the radiomic score was significant predictor of PFS (p < 0.001). The radiomic score was an independent prognostic biomarker of survival in DLBCL patients. The extraction of RFs from baseline 18 F-FDG-PET might be proposed in DLBCL to stratify high-risk versus low-risk patients of relapse after first-line therapy, especially in low-IPI patients.

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients

Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.

Surgical Treatment of Hepatocellular Carcinoma: Multicenter Competing-risk Analysis of Tumor-related Death Following Liver Resection and Transplantation Under an Intention-to-treat Perspective

BACKGROUND

Early-stage hepatocellular carcinoma could benefit from upfront liver resection (LR) or liver transplantation (LT), but the optimal strategy in terms of tumor-related outcomes is still debated. We compared the oncological outcomes of LR and LT for hepatocellular carcinoma, stratifying the study population into a low-, intermediate-, and high-risk class according to the risk of death at 5-y predicted by a previously developed prognostic model. The impact of tumor pathology on oncological outcomes of low- and intermediate-risk patients undergoing LR was investigated as a secondary outcome.

METHODS

We performed a retrospective multicentric cohort study involving 2640 patients consecutively treated by LR or LT from 4 tertiary hepatobiliary and transplant centers between 2005 and 2015, focusing on patients amenable to both treatments upfront. Tumor-related survival and overall survival were compared under an intention-to-treat perspective.

RESULTS

We identified 468 LR and 579 LT candidates: 512 LT candidates underwent LT, whereas 68 (11.7%) dropped-out for tumor progression. Ninety-nine high-risk patients were selected from each treatment cohort after propensity score matching. Three and 5-y cumulative incidence of tumor-related death were 29.7% and 39.5% versus 17.2% and 18.3% for LR and LT group ( P = 0.039), respectively. Low-risk and intermediate-risk patients treated by LR and presenting satellite nodules and microvascular invasion had a significantly higher 5-y incidence of tumor-related death (29.2% versus 12.5%; P < 0.001).

CONCLUSIONS

High-risk patients showed significantly better intention-to-treat tumor-related survival after upfront LT rather than LR. Cancer-specific survival of low- and intermediate-risk LR patients was significantly impaired by unfavorable pathology, suggesting the application of ab-initio salvage LT in such scenarios.

Blood- and Imaging-Derived Biomarkers for Oncological Outcome Modelling in Oropharyngeal Cancer: Exploring the Low-Hanging Fruit

Aims: To assess whether CT-based radiomics and blood-derived biomarkers could improve the prediction of overall survival (OS) and locoregional progression-free survival (LRPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent RT. Methods: Consecutive OPC patients with primary tumors treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status, and blood parameters (baseline hemoglobin levels, neutrophils, monocytes, and platelets, and derived measurements). Radiomic features were extracted from the gross tumor volumes (GTVs) of the primary tumor using pyradiomics. Outcomes of interest were LRPFS and OS. Following feature selection, a radiomic score (RS) was calculated for each patient. Significant variables, along with age and gender, were included in multivariable analysis, and models were retained if statistically significant. The models’ performance was compared by the C-index. Results: One hundred and five patients, predominately male (71%), were included in the analysis. The median age was 59 (IQR: 52–66) years, and stage IVA was the most represented (70%). HPV status was positive in 63 patients, negative in 7, and missing in 35 patients. The median OS follow-up was 6.3 (IQR: 5.5–7.9) years. A statistically significant association between low Hb levels and poorer LRPFS in the HPV-positive subgroup (p = 0.038) was identified. The calculation of the RS successfully stratified patients according to both OS (log-rank p < 0.0001) and LRPFS (log-rank p = 0.0002). The C-index of the clinical and radiomic model resulted in 0.82 [CI: 0.80–0.84] for OS and 0.77 [CI: 0.75–0.79] for LRPFS. Conclusions: Our results show that radiomics could provide clinically significant informative content in this scenario. The best performances were obtained by combining clinical and quantitative imaging variables, thus suggesting the potential of integrative modeling for outcome predictions in this setting of patients.

Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL

Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. Here, we hypothesized that distinct metabolic gene expression profiling (GEP) signatures might be correlated with outcome and with specific fluorodeoxyglucose positron emission tomography (FDG-PET) radiomic profiles in diffuse large B-cell lymphoma (DLBCL). We retrospectively analyzed a discovery cohort of 48 consecutive patients with DLBCL treated at our center with standard first-line chemoimmunotherapy by performing targeted GEP (T-GEP)- and FDG-PET radiomic analyses on the same target lesions at baseline. T-GEP-based metabolic profiling identified a 6-gene signature independently associated with outcomes in univariate and multivariate analyses. This signature included genes regulating mitochondrial oxidative metabolism (SCL25A1, PDK4, PDPR) that were upregulated and was inversely associated with genes involved in hypoxia and glycolysis (MAP2K1, HIF1A, GBE1) that were downregulated. These data were validated in 2 large publicly available cohorts. By integrating FDG-PET radiomics and T-GEP, we identified a radiometabolic signature (RadSig) including 4 radiomic features (histo kurtosis, histo energy, shape sphericity, and neighboring gray level dependence matrix contrast), significantly associated with the metabolic GEP-based signature (r = 0.43, P = .0027) and with progression-free survival (P = .028). These results were confirmed using different target lesions, an alternative segmentation method, and were validated in an independent cohort of 64 patients. RadSig retained independent prognostic value in relation to the International Prognostic Index score and metabolic tumor volume (MTV). Integration of RadSig and MTV further refined prognostic stratification. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for noninvasive assessment of cancer metabolism and prognostic stratification in DLBCL.

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Background

No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.

Methods

Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions.

Results

Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models).

Conclusions

Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.

Single fraction ablative preoperative radiation treatment for early-stage breast cancer: the CRYSTAL study – a phase I/II clinical trial protocol

Background

Breast-conserving surgery (BCS) and whole breast radiation therapy (WBRT) are the standard of care for early-stage breast cancer (BC). Based on the observation that most local recurrences occurred near the tumor bed, accelerated partial breast irradiation (APBI), consisting of a higher dose per fraction to the tumor bed over a reduced treatment time, has been gaining ground as an attractive alternative in selected patients with low-risk BC. Although more widely delivered in postoperative setting, preoperative APBI has also been investigated in a limited, though increasing, and number of studies. The aim of this study is to test the feasibility, safety and efficacy of preoperative radiotherapy (RT) in a single fraction for selected BC patients.

Methods

This is a phase I/II, single-arm and open-label single-center clinical trial using CyberKnife. The clinical investigation is supported by a preplanning section which addresses technical and dosimetric issues. The primary endpoint for the phase I study, covering the 1st and 2nd year of the research project, is the identification of the maximum tolerated dose (MTD) which meets a specific target toxicity level (no grade 3–4 toxicity). The primary endpoint for the phase II study (3rd to 5th year) is the evaluation of treatment efficacy measured in terms of pathological complete response rate.

Discussion

The study will investigate the response of BC to the preoperative APBI from different perspectives. While preoperative APBI represents a form of anticipated boost, followed by WBRT, different are the implications for the scientific community. The study may help to identify good responders for whom surgery could be omitted. It is especially appealing for patients unfit for surgery due to advanced age or severe co-morbidities, in addition to or instead of systemic therapies, to ensure long-term local control. Moreover, patients with oligometastatic disease synchronous with primary BC may benefit from APBI on the intact tumor in terms of tumor progression free survival. The study of response to RT can provide useful information about BC radiobiology, immunologic reactions, genomic expression, and radiomics features, to be tested on a larger scale.

Trial registration

The study was prospectively registered at clinicaltrials.gov (NCT04679454).

HeLLePhant: A phantom mimicking non-small cell lung cancer for texture analysis in CT images

PURPOSE

Phantoms mimicking human tissue heterogeneity and intensity are required to establish radiomic features robustness in Computed Tomography (CT) images. We developed inserts with two different techniques for the radiomic study of Non-Small Cell Lung Cancer (NSCLC) lesions.

METHODS

We developed two insert prototypes: two 3D-printed made of glycol-modified polyethylene terephthalate (PET-G), and nine with sodium polyacrylate plus iodinated contrast medium. The inserts were put in a handcraft phantom (HeLLePhant). We also analysed four materials of a commercial homogeneous phantom (Catphan® 424) and collected 29 NSCLC patients for comparison. All the CT acquisitions were performed with the same clinical protocol and scanner at 120kVp. The HeLLePhant phantom was scanned ten times in fixed condition at 120kVp and 100kVp for repeatability investigation. We extracted 153 radiomic features using Pyradiomics. To compare the features between phantoms and patients, we computed how many phantom features fell in the range between 10th and 90th percentile of the corresponding patient values. We deemed repeatable the features with a coefficient of variation (CV) less than or equal to 0.10.

RESULTS

The best similarity with the patients was obtained with the polyacrylate inserts (55.6-90.2%), the worst with Catphan (15.7-19.0%). For the PET-G inserts 35.3% and 36.6% of the features match the patient range. We found high repeatability for all the inserts of the HeLLePhant phantom (74.3-100% at 120kVp, 75.7-97.9% at 100kVp), and observed a texture dependency in repeatability.

CONCLUSIONS

Our study shows a promising way to construct heterogeneous inserts mimicking a target tissue for radiomic studies.

Personalized Dosimetry in Targeted Radiation Therapy: A Look to Methods, Tools and Critical Aspects

Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in external beam radiation therapy (EBRT). The purpose of this paper is to provide a thorough introduction to the field of personalized dosimetry in TRT, explaining its rationale in the context of optimization and describing the currently available methodologies. After listing the main therapies currently employed, the clinical workflow for the absorbed dose calculation is described, based on works of the most experienced authors in the literature and recent guidelines. Moreover, the widespread software packages for internal dosimetry are presented and critical aspects discussed. Overall, a selection of the most important and recent articles about this topic is provided.

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images

Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.

Effect of fructose instead of glucose or sucrose on cardiometabolic markers: a systematic review and meta-analysis of isoenergetic intervention trials

CONTEXT

Free, or added, sugars are considered important determinants in the pandemics of obesity and associated chronic diseases, and fructose has emerged as the sugar of main concern.

OBJECTIVE

The aim of this review was to assess the evidence of the effects of isoenergetic replacement of fructose or high-fructose corn syrup (HFCS) for glucose or sucrose on cardiometabolic markers in controlled dietary intervention trials.

DATA SOURCES

The electronic databases PubMed/MEDLINE, the Cochrane Library, and Embase were searched from 1980 to May 5, 2020.

STUDY SELECTION

Studies were eligible if they measured at least one of the following outcomes: total cholesterol, low- and high-density lipoprotein cholesterol, triacylglycerols, apolipoprotein A1, apolipoprotein B, systolic blood pressure, diastolic blood pressure, fasting glucose, and body weight.

DATA EXTRACTION

For each outcome, the mean values and the corresponding measure of dispersion were extracted after the intervention or control diet.

DATA ANALYSIS

Fixed-effects and random-effects models were used to pool study-specific estimates. Between-study heterogeneity was assessed by the χ2 test and the I2 statistic and publication bias by the Egger test and funnel plots.

RESULTS

Twenty-five studies involving 1744 volunteers were identified. No significant effects were found when fructose or HFCS was substituted for glucose, except for a slight decrease in diastolic blood pressure when fructose was substituted for glucose. Similarly, no effects were found when fructose or HFCS was substituted for sucrose, except for a small increase, of uncertain clinical significance, of apolipoprotein B when HFCS was substituted for sucrose.

CONCLUSIONS

Isoenergetic substitution of fructose or HFCS for glucose or sucrose has no significant effect on most of the cardiometabolic markers investigated; however, some results were affected by residual between-study heterogeneity and studies with high or unclear risk of bias.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42016042930.

MC1R variants and cutaneous melanoma risk according to histological type, body site, and Breslow thickness: a pooled analysis from the M-SKIP project

Little is known on whether melanocortin 1 receptor (MC1R) associated cutaneous melanoma (CM) risk varies depending on histological subtype and body site, and whether tumour thickness at diagnosis (the most important prognostic factor for CM patients) differs between MC1R variant carriers and wild-type individuals. We studied the association between MC1R variants and CM risk by histological subtype, body site, and Breslow thickness, using the database of the M-SKIP project. We pooled individual data from 15 case-control studies conducted during 2005-2015 in Europe and the USA. Study-specific, multi-adjusted odds ratios were pooled into summary odds ratios (SOR) and 95% confidence intervals (CI) using random-effects models. Six thousand eight hundred ninety-one CM cases and 5555 controls were included. CM risk was increased among MC1R variant carriers vs. wild-type individuals. The increase in risk was comparable across histological subtypes (SOR for any variant vs. wild-type ranged between 1.57 and 1.70, always statistical significant) except acral lentiginous melanoma (ALM), for which no association emerged; and slightly greater on chronically (1.74, 95% CI 1.47-2.07) than intermittently (1.55, 95% CI 1.34-1.78) sun-exposed skin. CM risk was greater for those carrying 'R' vs. 'r' variants; correlated with the number of variants; and was more evident among individuals not showing the red hair colour phenotype. Breslow thickness was not associated with MC1R status. MC1R variants were associated with an increased risk of CM of any histological subtype (except ALM) and occurring on both chronically and intermittently sun-exposed skin.

A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics

PURPOSE

To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features.

METHODS

T₂ -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images.

RESULTS

From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information.

CONCLUSION

We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.

MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218)

OBJECTIVES

Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa).

METHODS

This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis.

RESULTS

Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94.

CONCLUSIONS

MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice.

KEY POINTS

• A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains.

Association of quantitative MRI-based radiomic features with prognostic factors and recurrence rate in oropharyngeal squamous cell carcinoma

Radiomics focuses on extracting a large number of quantitative imaging features and testing both their correlation with clinical characteristics and their prognostic and predictive values. We propose a radiomic approach using magnetic resonance imaging (MRI) to decode the tumor phenotype and local recurrence in oropharyngeal squamous cell carcinoma (OPSCC). The contrast-enhanced T1-weighted sequences from baseline MRI examinations of OPSCC patients treated between 2008 and 2016 were retrospectively selected. Radiomic features were extracted using the IBEX software, and hiegrarchical clustering was applied to reduce features redundancy. The association of each radiomic feature with tumor grading and stage, HPV status, loco-regional recurrence within 2 years, considered as main endpoints, was assessed by univariate analysis and then corrected for multiple testing. Statistical analysis was performed with SAS/STAT® software. Thirty-two eligible cases were identified. For each patient, 1286 radiomic features were extracted, subsequently grouped into 16 clusters. Higher grading (G3 vs. G1/G2) was associated with lower values of GOH/65Percentile and GOH/85Percentile features (p=0.04 and 0.01, respectively). Positive HPV status was associated with higher values of GOH/10Percentile (p=0.03) and lower values of GOH/90Percentile (p=0.03). Loco-regional recurrence within 2 years was associated with higher values of GLCM3/4-7Correlation (p=0.04) and lower values of GLCM3/2-1InformationMeasureCorr1 (p=0.04). Results lost the statistical significance after correction for multiple testing. T stage was significantly correlated with 9 features, 4 of which (GLCM25/180-4InformationMeasureCorr2, Shape/MeanBreadth, GLCM25/90-1InverseDiffMomentNorm, and GLCM3/6-1InformationMeasureCorr1) retained statistical significance after False Discovery Rate correction. MRI-based radiomics is a feasible and promising approach for the prediction of tumor phenotype and local recurrence in OPSCC. Some radiomic features seem to be correlated with tumor characteristics and oncologic outcome however, larger collaborative studies are warranted in order to increase the statistical power and to obtain robust and validated results.

OpenDose: Open-Access Resource for Nuclear Medicine Dosimetry

Radiopharmaceutical dosimetry depends on the localization in space and time of radioactive sources and requires the estimation of the amount of energy emitted by the sources deposited within targets. In particular, when computing resources are not accessible, this task can be performed using precomputed tables of specific absorbed fractions (SAFs) or S values based on dosimetric models. The aim of the OpenDose collaboration is to generate and make freely available a range of dosimetric data and tools. Methods: OpenDose brings together resources and expertise from 18 international teams to produce and compare traceable dosimetric data using 6 of the most popular Monte Carlo codes in radiation transport (EGSnrc/EGS++, FLUKA, GATE, Geant4, MCNP/MCNPX, and PENELOPE). SAFs are uploaded, together with their associated statistical uncertainties, in a relational database. S values are then calculated from monoenergetic SAFs on the basis of the radioisotope decay data presented in International Commission on Radiological Protection Publication 107. Results: The OpenDose collaboration produced SAFs for all source region and target combinations of the 2 International Commission on Radiological Protection Publication 110 adult reference models. SAFs computed from the different Monte Carlo codes were in good agreement at all energies, with SDs below individual statistical uncertainties. Calculated S values were in good agreement with OLINDA/EXM 2.0 (commercial) and IDAC-Dose 2.1 (free) software. A dedicated website (www.opendose.org) has been developed to provide easy and open access to all data. Conclusion: The OpenDose website allows the display and downloading of SAFs and the corresponding S values for 1,252 radionuclides. The OpenDose collaboration, open to new research teams, will extend data production to other dosimetric models and implement new free features, such as online dosimetric tools and patient-specific absorbed dose calculation software, together with educational resources.

PETER PHAN: An MRI phantom for the optimisation of radiomic studies of the female pelvis

PURPOSE

To develop a phantom for methodological radiomic investigation on Magnetic Resonance (MR) images of female patients affected by pelvic cancer.

METHODS

A pelvis-shaped container was filled with a MnCl₂ solution reproducing the relaxation times (T₁, T₂) of muscle surrounding pelvic malignancies. Inserts simulating multi-textured lesions were embedded in the phantom. The relaxation times of muscle and tumour were measured on an MR scanner on healthy volunteers and patients; T₁ and T₂ of MnCl₂ solutions were evaluated with a relaxometer to find the concentrations providing a match to in vivo relaxation times. Radiomic features were extracted from the phantom inserts and the patients' lesions. Their repeatability was assessed by multiple measurements.

RESULTS

Muscle T₁ and T₂ were 1128 (806-1378) and 51 (40-65) ms, respectively. The phantom reproduced in vivo values within 13% (T₁) and 12% (T₂). T₁ and T₂ of tumour tissue were 1637 (1396-2121) and 94 (79-101) ms, respectively. The phantom insert best mimicking the tumour agreed within 7% (T₁) and 24% (T₂) with in vivo values. Out of 1034 features, 75% (95%) had interclass correlation coefficient greater than 0.9 on T₁ (T₂)-weighted images, reducing to 33% (25%) if the phantom was repositioned. The most repeatable features on phantom showed values in agreement with the features extracted from patients' lesions.

CONCLUSIONS

We developed an MR phantom with inserts mimicking both relaxation times and texture of pelvic tumours. As exemplified with repeatability assessment, such phantom is useful to investigate features robustness and optimise the radiomic workflow on pelvic MR images.

The Impact of Sarcopenia on Postoperative Course following Pancreatoduodenectomy: Single-Center Experience of 110 Consecutive Cases

BACKGROUND

Despite that mortality following pancreatoduodenectomy (PD) has gradually dropped during the past few decades, the incidence of postoperative complications remains high, ranging from 30-60%. Many studies have been focused on identification of perioperative risk factors for morbidity, and in recent years, sarcopenia has been pointed out as a valid predictor of postoperative complication.

MATERIALS AND METHODS

Perioperative data from 110 consecutive patients who underwent PD were retrieved, and the presence of sarcopenia was assessed by the measurement of Hounsfield unit average calculation on preoperative CT scans. Postoperative complications were graded according to Clavien-Dindo classification, and the morbidity burden was assessed by comprehensive complication index (CCI) calculation.

RESULTS

Sarcopenia was associated with advanced age (72 vs. 66 years; p = 0.014) and lower preoperative albumin levels (3.5 vs. 3.7 g/dL; p = 0.010); it represented an independent risk factor for clinically relevant complications (relative risk: 1.71; p = 0.015) and was related to a higher rate of Grade C postoperative pancreatic fistula (50.0 vs. 11.4%; p = 0.005) and a higher CCI (47.6 vs. 29.6; p = 0.001).

CONCLUSIONS

Sarcopenia represents a valid indicator of increased morbidity risk and may play a central role in preoperative risk stratification, allowing the selection of patients who may benefit from prehabilitation programs.

Immediate breast reconstruction with latissimus dorsi flap for patients with local recurrence of breast cancer

BACKGROUND

Ipsilateral breast cancer recurrence (IBTR) occurs in about 7% of patients with primary invasive breast tumor. Salvage mastectomy and breast reconstruction are often discussed and latissimus dorsi (LD) flap is frequently proposed.

METHODS

We retrospectively investigated 111 consecutive locally relapsing patients who underwent salvage mastectomy and immediate LD reconstruction. All included patients with IBTR previously underwent conserving surgery for BC, and received a postoperative irradiation. Primary endpoints were disease free survival and overall survival. Secondary endpoints were surgical complications and re-interventions.

RESULTS

Invasive ductal cancer was the most frequent histotype (60.4%) of breast cancer reappearance. rpT1, rpT2 and rpT3 were observed respectively in 50.5%, 20,7% and 3,6% of the patients. rpTis occurred in 11,7% of cases. Positive axillary nodes were observed in 9,9% of patients at reappearance. Post-operative complication other than seroma occurred in 17,1% of patients, while seroma at the donor site was observed in 61.3% of cases. At 5-year after surgery overall survival was 92% (95% CI: 85%-96%) and disease free survival was 78% (95% CI: 69%-85%).

CONCLUSIONS

Immediate latissimus dorsi flap reconstruction in selected patients with isolated breast tumor recurrence, which occurred after breast irradiation, provides an effective treatment with a satisfactory outcome.

Compliance to Adjuvant Chemotherapy of Patients Who Underwent Surgery for Rectal Cancer: Report from a Multi-institutional Research Network

INTRODUCTION

Adjuvant chemotherapy for locally advanced rectal cancer is associated with improved overall survival. However, recent evidence from randomized trials showed a compliance rate of 43 to 73%, which may affect efficacy. The aim of this multicenter retrospective analysis was to investigate the compliance rate to adjuvant treatment for patients who underwent rectal surgery for cancer.

METHODS

Patients who underwent surgery with curative intent for rectal cancer in six Italian colorectal centers between January 2013 and December 2017 were retrospectively reviewed. Exclusion criteria were age less than 18 years, palliative or emergency surgery, and stage IV disease. Parameters of interest were patients' characteristics, preoperative tumor stage, neo-adjuvant chemoradiation therapy, intra-operative and postoperative outcomes. Although the participating centers referred to the same treatment guidelines for treatment, the chemotherapy regiment was not standardized across the institutions. Reasons for not starting adjuvant chemotherapy when indicated, interruption, and modification of drug regimen were collected to investigate compliance.

RESULTS

A total of 572 patients were included in the analysis. Two hundred and fifty-two (44.1%) patients received neo-adjuvant chemoradiation therapy. All patients underwent high anterior rectal resection, low anterior rectal resection, or Miles' procedure. Of 399 patients with an indication to adjuvant chemotherapy, 176 (44.1%) completed the treatment as planned. Compliance for patients who started chemotherapy was 56% (95% CI 50.4-61.6%). Sixty-six patients interrupted the treatment, 76 patients significantly reduced the drug dose, and 41 patients had to switch to other therapeutic regimens.

CONCLUSIONS

The present multicenter investigation reports a low compliance rate to adjuvant chemotherapy after rectal resection for cancer. Multidisciplinary teams should focus on future effort to improve compliance for these patients.

MC1R variants in childhood and adolescent melanoma: a retrospective pooled analysis of a multicentre cohort

BACKGROUND

Germline variants in the melanocortin 1 receptor gene (MC1R) might increase the risk of childhood and adolescent melanoma, but a clear conclusion is challenging because of the low number of studies and cases. We assessed the association of MC1R variants with childhood and adolescent melanoma in a large study comparing the prevalence of MC1R variants in child or adolescent patients with melanoma to that in adult patients with melanoma and in healthy adult controls.

METHODS

In this retrospective pooled analysis, we used the M-SKIP Project, the Italian Melanoma Intergroup, and other European groups (with participants from Australia, Canada, France, Greece, Italy, the Netherlands, Serbia, Spain, Sweden, Turkey, and the USA) to assemble an international multicentre cohort. We gathered phenotypic and genetic data from children or adolescents diagnosed with sporadic single-primary cutaneous melanoma at age 20 years or younger, adult patients with sporadic single-primary cutaneous melanoma diagnosed at age 35 years or older, and healthy adult individuals as controls. We calculated odds ratios (ORs) for childhood and adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged 18 or younger and 14 years or younger.

FINDINGS

We analysed data from 233 young patients, 932 adult patients, and 932 healthy adult controls. Children and adolescents had higher odds of carrying MC1R r variants than did adult patients (OR 1·54, 95% CI 1·02-2·33), including when analysis was restricted to patients aged 18 years or younger (1·80, 1·06-3·07). All investigated variants, except Arg160Trp, tended, to varying degrees, to have higher frequencies in young patients than in adult patients, with significantly higher frequencies found for Val60Leu (OR 1·60, 95% CI 1·05-2·44; p=0·04) and Asp294His (2·15, 1·05-4·40; p=0·04). Compared with those of healthy controls, young patients with melanoma had significantly higher frequencies of any MC1R variants.

INTERPRETATION

Our pooled analysis of MC1R genetic data of young patients with melanoma showed that MC1R r variants were more prevalent in childhood and adolescent melanoma than in adult melanoma, especially in patients aged 18 years or younger. Our findings support the role of MC1R in childhood and adolescent melanoma susceptibility, with a potential clinical relevance for developing early melanoma detection and preventive strategies.

FUNDING

SPD-Pilot/Project-Award-2015; AIRC-MFAG-11831.

Radiomics: the facts and the challenges of image analysis

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Radiomics: the facts and the challenges of image analysis

Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Each step needs careful evaluation for the construction of robust and reliable models to be transferred into clinical practice for the purposes of prognosis, non-invasive disease tracking, and evaluation of disease response to treatment. After the definition of texture parameters (shape features; first-, second-, and higher-order features), we briefly discuss the origin of the term radiomics and the methods for selecting the parameters useful for a radiomic approach, including cluster analysis, principal component analysis, random forest, neural network, linear/logistic regression, and other. Reproducibility and clinical value of parameters should be firstly tested with internal cross-validation and then validated on independent external cohorts. This article summarises the major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomography, and magnetic resonance imaging.

Liver Grafts From Donors After Circulatory Death on Regional Perfusion With Extended Warm Ischemia Compared With Donors After Brain Death

Donation after circulatory death (DCD) in Italy constitutes a relatively unique population because of the requirement of a no-touch period of 20 minutes. The first aim of this study was to compare liver transplantations from donors who were maintained on normothermic regional perfusion after circulatory death and suffered extended warm ischemia (DCD group, n = 20) with those from donors who were maintained on extracorporeal membrane oxygenation (ECMO) and succumbed to brain death (ECMO group, n = 17) and those from standard donors after brain death (donation after brain death [DBD] group, n = 52). Second, we conducted an explorative analysis on the DCD group to identify relationships between the donor characteristics and the transplant outcomes. The 1-year patient survival for the DCD group (95%) was not significantly different from that of the ECMO group (87%; P = 0.47) or the DBD group (94%; P = 0.94). Graft survival was slightly inferior in the DCD group (85%) because of a high rate of primary nonfunction (10%) and retransplantation (15%) but was not significantly different from the ECMO group (87%; P = 0.76) or the DBD group (91%; P = 0.20). Although ischemic cholangiopathy was more frequent in the DCD group (10%), this issue did not adversely impact graft survival because none of the recipients underwent retransplantation due to biliary complications. Moreover, the DCD recipients were more likely to develop posttransplant renal dysfunction with the need for renal replacement therapy. Further analysis of the DCD group showed that warm ischemia >125 minutes and an Ishak fibrosis score of 1 at liver biopsy negatively impacted serum creatinine and alanine transaminase levels in the first posttransplant week, respectively. In conclusion, our findings encourage the use of liver grafts from DCD donors maintained by regional perfusion after proper selection.

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months

OBJECTIVES

To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients.

METHODS

This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant.

RESULTS

Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model.

CONCLUSION

This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12.

KEY POINTS

• No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.

Cholestasis is the Main Determinant of Abnormal CA 19–9 Levels in Patients with Liver Cirrhosis

Background/Aims

Altered CA19–9 levels are commonly found in patients with liver cirrhosis though a clear explanation for this finding has not yet been given. The aim of this study was to investigate whether CA19–9 levels might be related to alterations in biochemical parameters and/or to functional impairment in cirrhotic patients with and without hepatocellular carcinoma. Methods: We studied 126 patients with liver cirrhosis, 60 of whom also had hepatocellular carcinoma. CA19–9 values were related to clinical, biochemical and functional parameters. In half of the patients CA19–9 levels were related to the monoethylglycinexylidide test, which is a dynamic liver function test.

Results

In more than half the cases CA19–9 values were above the upper limit. Liver function worsening as assessed by Child-Pugh's score and monoethylglycinexylidide test did not seem to influence the alteration of the marker. By contrast, in univariate analysis CA19–9 correlated with aminotransferases, γ-glutamyltransferase and alkaline phosphatase. Multivariate analysis showed that besides alkaline phosphatase also the presence of hepatocellular carcinoma might influence the alteration of CA19–9, although the marker was of no use for the diagnosis of liver cancer in patients with altered though not diagnostic α-fetoprotein levels.

Conclusions

In our study we confirmed the correlation of CA19–9 levels with cholestasis and cytolysis parameters. Moreover, we found no association between CA19–9 levels and impaired liver function as assessed by means of the Child-Pugh's score and the monoethylglycinexylidide test, which is cholestasis-independent and explores liver metabolic and clearance activities. The cholestatic picture that characterizes liver cirrhosis might enhance the expression and passage of the marker from the bile to the blood. The addition of CA19–9 assessment is not useful for the diagnosis of hepatocellular carcinoma in patients with non-diagnostic levels of α-fetoprotein. Caution should therefore be used when evaluating CA19–9 in cirrhotic patients with cholestasis, since false positive results may occur.

Role of interim 18F-FDG-PET/CT for the early prediction of clinical outcomes of Non-Small Cell Lung Cancer (NSCLC) during radiotherapy or chemo-radiotherapy. A systematic review

BACKGROUND

Non-Small Cell Lung Cancer (NSCLC) is characterized by aggressiveness and includes the majority of thorax malignancies. The possibility of early stratification of patients as responsive and non-responsive to radiotherapy with a non-invasive method is extremely appealing. The distribution of the Fluorodeoxyglucose (¹⁸F-FDG) in tumours, provided by Positron-Emission-Tomography (PET) images, has been proved to be useful to assess the initial staging of the disease, recurrence, and response to chemotherapy and chemo-radiotherapy (CRT).

OBJECTIVES

In the last years, particular efforts have been focused on the possibility of using ad interim ¹⁸F-FDG PET (FDG_int) to evaluate response already in the course of radiotherapy. However, controversial findings have been reported for various malignancies, although several results would support the use of FDG_int for individual therapeutic decisions, at least in some pathologies. The objective of the present review is to assemble comprehensively the literature concerning NSCLC, to evaluate where and whether FDG_int may offer predictive potential.

METHODS

Several searches were completed on Medline and the Embase database, combining different keywords. Original papers published in the English language from 2005 to 2016 with studies involving FDG_int in patients affected by NSCLC and treated with radiation therapy or chemo-radiotherapy only were chosen.

RESULTS

Twenty-one studies out of 970 in Pubmed and 1256 in Embase were selected, reporting on 627 patients.

CONCLUSION

Certainly, the lack of univocal PET parameters was identified as a major drawback, while standardization would be required for best practice. In any case, all these papers denoted FDG_int as promising and a challenging examination for early assessment of outcomes during CRT, sustaining its predictivity in lung cancer.

Arginase inhibition suppresses lung metastasis in the 4T1 breast cancer model independently of the immunomodulatory and anti-metastatic effects of VEGFR-2 blockade

Tumor angiogenesis promotes tumor growth and metastasis. Anti-angiogenic therapy in combination with chemotherapy is used for the treatment of metastatic cancers, including breast cancer but therapeutic benefits are limited. Mobilization and accumulation of myeloid-derived suppressor cells (MDSC) during tumor progression and therapy have been implicated in metastasis formation and resistance to anti-angiogenic treatments. Here, we used the 4T1 orthotopic syngenic mouse model of mammary adenocarcinoma to investigate the effect of VEGF/VEGFR-2 axis inhibition on lung metastasis, MDSC and regulatory T cells (Tregs). We show that treatment with the anti-VEGFR-2 blocking antibody DC101 inhibits primary tumor growth, angiogenesis and lung metastasis. DC101 treatment had no effect on MDSC mobilization, but partially attenuated the inhibitory effect of mMDSC on T cell proliferation and decreased the frequency of Tregs in primary tumors and lung metastases. Strikingly, DC101 treatment induced the expression of the immune-suppressive molecule arginase I in mMDSC. Treatment with the arginase inhibitor N^ω-hydroxy-nor-Arginine (Nor-NOHA) reduced the inhibitory effect of MDSC on T cell proliferation and inhibited number and size of lung metastasis but had little or no additional effects in combination with DC101. In conclusion, DC101 treatment suppresses 4T1 tumor growth and metastasis, partially reverses the inhibitory effect of mMDSC on T cell proliferation, decreases Tregs in tumors and increases arginase I expression in mMDSC. Arginase inhibition suppresses lung metastasis independently of DC101 effects. These observations contribute to the further characterization of the immunomodulatory effect of anti-VEGF/VEGFR2 therapy and provide a rationale to pursue arginase inhibition as potential anti-metastatic therapy.

Impact of SPECT corrections on 3D-dosimetry for liver transarterial radioembolization using the patient relative calibration methodology

PURPOSE

Many centers aim to plan liver transarterial radioembolization (TARE) with dosimetry, even without CT-based attenuation correction (AC), or with unoptimized scatter correction (SC) methods. This work investigates the impact of presence vs absence of such corrections, and limited spatial resolution, on 3D dosimetry for TARE.

METHODS

Three voxelized phantoms were derived from CT images of real patients with different body sizes. Simulations of (99m)Tc-SPECT projections were performed with the SIMIND code, assuming three activity distributions in the liver: uniform, inside a "liver's segment," or distributing multiple uptaking nodules ("nonuniform liver"), with a tumoral liver/healthy parenchyma ratio of 5:1. Projection data were reconstructed by a commercial workstation, with OSEM protocol not specifically optimized for dosimetry (spatial resolution of 12.6 mm), with/without SC (optimized, or with parameters predefined by the manufacturer; dual energy window), and with/without AC. Activity in voxels was calculated by a relative calibration, assuming identical microspheres and (99m)Tc-SPECT counts spatial distribution. 3D dose distributions were calculated by convolution with (90)Y voxel S-values, assuming permanent trapping of microspheres. Cumulative dose-volume histograms in lesions and healthy parenchyma from different reconstructions were compared with those obtained from the reference biodistribution (the "gold standard," GS), assessing differences for D95%, D70%, and D50% (i.e., minimum value of the absorbed dose to a percentage of the irradiated volume). γ tool analysis with tolerance of 3%/13 mm was used to evaluate the agreement between GS and simulated cases. The influence of deep-breathing was studied, blurring the reference biodistributions with a 3D anisotropic gaussian kernel, and performing the simulations once again.

RESULTS

Differences of the dosimetric indicators were noticeable in some cases, always negative for lesions and distributed around zero for parenchyma. Application of AC and SC reduced systematically the differences for lesions by 5%-14% for a liver segment, and by 7%-12% for a nonuniform liver. For parenchyma, the data trend was less clear, but the overall range of variability passed from -10%/40% for a liver segment, and -10%/20% for a nonuniform liver, to -13%/6% in both cases. Applying AC, SC with preset parameters gave similar results to optimized SC, as confirmed by γ tool analysis. Moreover, γ analysis confirmed that solely AC and SC are not sufficient to obtain accurate 3D dose distribution. With breathing, the accuracy worsened severely for all dosimetric indicators, above all for lesions: with AC and optimized SC, -38%/-13% in liver's segment, -61%/-40% in the nonuniform liver. For parenchyma, D50% resulted always less sensitive to breathing and sub-optimal correction methods (difference overall range: -7%/13%).

CONCLUSIONS

Reconstruction protocol optimization, AC, SC, PVE and respiratory motion corrections should be implemented to obtain the best possible dosimetric accuracy. On the other side, thanks to the relative calibration, D50% inaccuracy for the healthy parenchyma from absence of AC was less than expected, while the optimization of SC was scarcely influent. The relative calibration therefore allows to perform TARE planning, basing on D50% for the healthy parenchyma, even without AC or with suboptimal corrections, rather than rely on nondosimetric methods.

Effects of free sugars on blood pressure and lipids: a systematic review and meta-analysis of nutritional isoenergetic intervention trials

BACKGROUND

Sugar has been suggested as a central risk factor in the development of noncommunicable diseases.

OBJECTIVE

We assessed the evidence of the effects of free sugars compared with complex carbohydrates on selected cardiovascular disease risk factors.

DESIGN

We conducted a systematic review and meta-analysis of intervention trials to compare diets that provide a given amount of energy from free sugars with a control diet that provides the same amount of energy from complex carbohydrates. The primary outcomes were: blood pressure, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triacylglycerols, apolipoproteins A-I and B, or very low-density lipoprotein cholesterol. Body weight was also recorded but was not a primary outcome of the studies.

RESULTS

In all, 28 studies involving 510 volunteers were included. When free sugars were substituted for complex carbohydrates, no significant increases were detected in systolic or diastolic blood pressure, and no heterogeneity was observed. There were significant increases in HDL cholesterol, LDL cholesterol, and triacylglycerols, although for LDL cholesterol and triacylglycerols there was significant heterogeneity between studies and evidence of publication bias. After adjustment for missing studies, these increases lost significance. Subgroup analyses showed that diets providing the largest total energy intake and energy exchange enhanced the effect of free sugars on total and LDL cholesterol and triacylglycerols. The increase of triacylglycerols was no longer significant when studies with the highest risk of bias were excluded or when only randomized trials were considered. Free sugars had no effect on body weight.

CONCLUSIONS

In short- or moderate-term isoenergetic intervention trials, the substitution of free sugars for complex carbohydrates had no effect on blood pressure or body weight and an unclear effect on blood lipid profile. Further independent trials are required to assess whether the reduction of free sugars improves cardiovascular disease risk factors. This review was registered at http://www.crd.york.ac.uk/prospero as CRD42016042930.

Therapeutic schemes in 177Lu and 90Y-PRRT: radiobiological considerations

BACKGROUND

The purpose of this work is to implement a radiobiological model to compare different treatment schedules for Peptide Receptor Radionuclide Therapy (PRRT) with 177Lu and 90Y. The principal radiobiological quantities were studied as a function of radionuclides, fractionation schemes, activity distribution in kidneys and tumor radiosensitivity.

METHODS

Clinical data were used to derive representative absorbed doses for several treatment schemes for 177Lu-PRRT and for 90Y-PRRT and considered as input data for the radiobiological model. Both uniform and non-uniform activity distributions were considered for kidneys and cortex; for tumors a possible uptake reduction after each cycle and inter-patient radiosensitivity variability were investigated. Normal-Tissue-Complication-Probability (NTCP) and Tumor-Control-Probability (TCP) were evaluated.

RESULTS

Hyper-cycling has a limited advantage in terms of BED reduction on kidneys for 177Lu, while for 90Y the effect is sizable and helps in reducing the NTCP. For all 177Lu-schemes the renal toxicity risk is negligible while for some 90Y-schemes the NTCP is not null. In case of tumor uptake reduction with cycles the treatment efficacy is reduced with a BED loss up to 46%. The TCP decreases when assuming normally-distributed tumor radiosensitivity values.

CONCLUSIONS

This paper discusses how the combination of dosimetry and radiobiological modeling may help in exploring the link between the treatment schedule and the potential clinical outcome. The results highlight the capability of model to reproduce the available clinical data and provide useful qualitative information. Further investigation on dose distribution and dose uptake reduction with accurate clinical data is needed to progress in this field.

Lower Body vs. Upper Body Resistance Training and Arterial Stiffness in Young Men

Resistance training has been shown to increase arterial stiffness. The purpose of the present study was to examine and compare the systemic arterial stiffness responses to acute lower body (LRT) and upper body (URT) resistance training. 20 healthy young men [median age: 26 years (interquartile range 23, 32)] underwent LRT, URT and whole body resistance training (WRT). Before and immediately after, as well as 20, 40 and 60 min after each training session, we measured the cardio-ankle vascular index (CAVI) and brachial-ankle pulse wave velocity (baPWV) using VaSera VS-1500 N. We used mixed models for repeated measurements to estimate the post-exercise differences in CAVI and baPWV between the 3 resistance training modes. Immediately after exercise cessation, both CAVI and baPWV were lower for LRT compared with URT [CAVI: - 0.93 (95% confidence interval [CI] - 1.15, - 0.70); baPWV: - 2.08 m/s (95% CI - 2.48, - 1.67)]. Differences between LRT and URT gradually decreased during follow-up. Compared with WRT, LRT induced a decrease and URT an increase in arterial stiffness across all time points. In conclusion, LRT presents more favorable post-exercise arterial stiffness than URT. Our results suggest that LRT or WRT may be preferred over URT in individuals with impaired arterial stiffness.

Twenty years of radiobiology in clinical practice: the Italian contribution

AIMS AND BACKGROUND

To present the Italian state-of-the-art contribution to radiobiology of external beam radiotherapy, brachytherapy, and radionuclide radiotherapy.

METHODS AND STUDY DESIGN

A survey of the literature was carried out, using PubMed, by some independent researchers of the Italian group of radiobiology. Each paper was reviewed by researchers of centers not comprising its authors. The survey was limited to papers in English published over the last 20 years, written by Italian investigators or in Italian institutions, excluding review articles.

RESULTS

A total of 135 papers have been published in journals with an impact factor, with an increase in the number of published papers over time, for external beam radiotherapy rather than radionuclide radiotherapy. The quantity and quality of the papers researched constitutes a proof of the enduring interest in clinical radiobiology among Italian investigators.

CONCLUSIONS

The survey could be useful to individuate expert partners for an Italian network on clinical radiobiology, addressing future collaborative investigations.