A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

[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.

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.

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 ).

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.

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.

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.

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.

Correlation of dose with toxicity and tumour response to 90Y- and 177Lu-PRRT provides the basis for optimization through individualized treatment planning.

PURPOSE: Peptide receptor radionuclide therapy (PRRT) with 90Y-labelled and 177Lu-labelled peptides is an effective strategy for the treatment of metastatic/nonresectable neuroendocrine tumours (NETs). Dosimetry provides important information useful for optimizing PRRT with individualized regimens to reduce toxicity and increase tumour responses. However, this strategy is not applied in routine clinical practice, despite the fact that several dosimetric studies have demonstrated significant dose-effect correlations for normal organ toxicity and tumour response that can better guide therapy planning. The present study reviews the key relationships and the radiobiological models available in the literature with the aim of providing evidence that optimization of PRRT is feasible through the implementation of dosimetry. METHODS: The MEDLINE database was searched combining specific keywords. Original studies published in the English language reporting dose-effect outcomes in patients treated with PRRT were chosen. RESULTS: Nine of 126 studies were selected from PubMed, and a further five were added manually, reporting on 590 patients. The studies were analysed and are discussed in terms of weak and strong elements of correlations. CONCLUSION: Several studies provided evidence of clinical benefit from the implementation of dosimetry in PRRT, indicating the potential contribution of this approach to reducing severe toxicity and/or reducing undertreatment that commonly occurs. Prospective trials, possibly multicentre, with larger numbers of patients undergoing quantitative dosimetry and with standardized methodologies should be carried out to definitively provide robust predictive paradigms to establish effective tailored PRRT.

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.

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 (FDGint) 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 FDGint 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 FDGint 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 FDGint 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 FDGint as promising and a challenging examination for early assessment of outcomes during CRT, sustaining its predictivity in lung cancer.

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.

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.

Investigation of 90Y-avidin for prostate cancer brachytherapy: a dosimetric model for a phase I-II clinical study.

PURPOSE: A novel method for prostate irradiation is investigated. Similarly to (125)I or (103)Pd seed brachytherapy, (90)Y-avidin could be injected via the perineum under ultrasound image guidance. This study inspects the theoretical feasibility with a dosimetric model based on Monte Carlo simulation. METHODS: A geometrical model of the prostate, urethra and rectum was designed. The linear-quadratic model was applied to convert (125)I absorbed dose prescription/constraints into (90)Y dose through biological effective dose (BED) calculation. The optimal (90)Y-avidin injection strategy for the present model was obtained. Dose distribution was calculated by Monte Carlo simulation (PENELOPE,GEANT4). Dose volume histograms (DVH) for the prostate, urethra and rectum were compared to typical DVHs of (125)I seed brachytherapy, used routinely in our institute. RESULTS: With (90)Y-avidin, at least 95% of the prostate must receive more than 70 Gy. The absorbed dose to 10% of the urethra (D(10%_urethra)) and the maximum absorbed dose to the rectum (D(max_rectum)) must be lower than 122 Gy. For the present model, the optimum strategy consists in multiple injections of (90)Y-avidin 50 µl drops, for a total volume of 3.1 ml. The minimum activity to deliver the prescribed absorbed dose is 0.7 GBq, which also fully respects urethral and rectal constraints. The resulting dose map has a maximum in the central region with a sharp decrease towards the urethra and the prostate edge. Notably, D(10%_urethra) is 95 Gy and D(max_rectum) is below 2 Gy. Prostate absorbed dose is higher with (90)Y-avidin than (125)I seeds, although the total volume receiving the prescribed absorbed dose is 1-2% lower. Urethral DVH strictly depends on the (90)Y distribution, to be optimized according to prostate shape; in our model, BED(30%_urethra) is 90 Gy with (90)Y-avidin, whereas for patients receiving (125)I seeds it ranges between 150 and 230 Gy. The rectal DVH is always more favourable with (90)Y. CONCLUSION: The methodology is theoretically feasible and can deliver an effective treatment in T1-T2 prostate cancer. Pharmacokinetic and biodistribution studies in prostate cancer patients are needed for validation.

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.

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.

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.

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.