The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

PURPOSE: This study aims to further enhance a validated radiomics-based model for predicting pathologic complete response (pCR) after chemo­radiotherapy in locally advanced rectal cancer (LARC) for use in clinical practice. METHODS: A generalized linear model (GLM) to predict pCR in LARC patients previously trained in Europe and validated with an external inter-continental cohort (59 patients), was first examined with further 88 intercontinental patient datasets to assess its reproducibility; then new radiomics and clinical features, and validation methods were investigated to build a new model for enhancing the pCR prediction for patients admitted to our department. The patients were divided into training group (75%) and validation group (25%) according to their demographic. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the extracted features of the training group and select the optimal ones; the performance of the reference GLM and enhanced models was compared through the area under curve (AUC) of the receiver operating characteristics. RESULTS: The value of AUC of the reference model was 0.831 (95% CI, 0.701-0.961), and 0.828 (95% CI, 0.700-0.956) in the original and new validation cohorts, respectively, showing a reproducibility in the applicability of the GLM model. Eight features were found to be significant with LASSO and used to establish an enhanced model. The AUC of the enhanced model of 0.926 (95% CI, 0.859-0.993) for training, and 0.926 (95% CI, 0.767-1.00) for the validation group shows better performance than the reference model. CONCLUSIONS: The GLM model shows good reproducibility in predicting pCR in LARC; the enhanced model has the potential to improve prediction accuracy and may be a candidate in clinical practice.

Does restaging MRI radiomics analysis improve pathological complete response prediction in rectal cancer patients? A prognostic model development.

PURPOSE: Our study investigated the contribution that the application of radiomics analysis on post-treatment magnetic resonance imaging can add to the assessments performed by an experienced disease-specific multidisciplinary tumor board (MTB) for the prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). MATERIALS AND METHODS: This analysis included consecutively retrospective LARC patients who obtained a complete or near-complete response after nCRT and/or a pCR after surgery between January 2010 and September 2019. A three-step radiomics features selection was performed and three models were generated: a radiomics model (rRM), a multidisciplinary tumor board model (yMTB) and a combined model (CM). The predictive performance of models was quantified using the receiver operating characteristic (ROC) curve, evaluating the area under curve (AUC). RESULTS: The analysis involved 144 LARC patients; a total of 232 radiomics features were extracted from the MR images acquired post-nCRT. The yMTB, rRM and CM predicted pCR with an AUC of 0.82, 0.73 and 0.84, respectively. ROC comparison was not significant (p = 0.6) between yMTB and CM. CONCLUSION: Radiomics analysis showed good performance in identifying complete responders, which increased when combined with standard clinical evaluation; this increase was not statistically significant but did improve the prediction of clinical response.

EROS 2.0 study: evaluation of two interventional radiotherapy (brachytherapy) schedules for endometrial cancer: a comparison of late vaginal toxicity rates.

BACKGROUND: To compare the late toxicity rates after two different high dose rate (HDR) adjuvant intravaginal interventional radiotherapy (IRT-brachytherapy) dose schedules in stage I-II endometrial cancer. METHODS: Stage I-II patients with endometrial cancer treated with surgery (with or without lymphadenectomy) and adjuvant HDR-IRT between 2014 and 2020 were included in this analysis. Patients were treated with two schedules. In the first cohort (C1), 21 Gy were delivered in three weekly fractions (7 Gy) prescribed 0.5 cm from the applicator surface. In the second cohort (C2), 24 Gy were delivered in four weekly fractions (6 Gy). The clinical target volume was the upper third of the vagina for C1 and the upper 3 cm for C2. HDR-IRT technique and point prescription (5 mm depth from the applicator surface) were the same for all patients. Vaginal toxicity was scored according to the CTCAE 5.0 scale in terms of the presence versus absence of any toxicity grade. The correlation among toxicity and clinical covariates (age, lymphadenectomy, fractionation, stage) was tested by Pearson correlation test (univariate) and by logistic regression (multivariable). RESULTS: 114 stage I and three stage II patients, median age 62 (range: 32-85) years, were included in this analysis. The mean follow-up was 56.3 months in C1 (40-76) and 20 months in C2 (8-42). Vaginal late toxicity was recorded in 40 and 15 patients in C1 and 2, respectively. Age, lymphadenectomy, and fractionation were significantly correlated with toxicity at univariate analysis (p value = 0.029, 0.006, and 0.002, respectively), while stepwise logistic regression confirmed only age and fractionation as significantly correlated parameters (p value = 0.02 and 0.001, respectively). Three-year local relapse-free, distant metastasis-free and cause-specific survival rates were 96.6%, 94.8%, and 99.1%, respectively. CONCLUSIONS: This analysis showed lower vaginal late toxicity rate in C2 compared to C1.

Radiotherapy during the COVID-19: a review about management and treatment strategies.

Background: The administration of radiotherapy should be encouraged despite the emergency of COVID-19; therefore, our aim is to analyze management and therapeutic interventions to be implemented in a Radiotherapy department to allow patients to continue their treatment and health professionals to continue their work safely. Materials and methods: A Pubmed search was performed, in which all articles specific to Radiotherapy and COVID-19 were included. Those articles that were too specific about the COVID-19, surgery and chemiotherapy, were excluded. Results: 315 articles were selected, of which 35 were about therapeutic strategies and 25 about management strategies. In the first category, 5 articles were about how radiotherapy could be a weapon to be used for COVID-19 positive patients with important lung problems. While 30 articles described priorities and new treatment plans for oncology patients who have to undergo radiotherapy during the pandemic. In the second category, almost all the articles explained how triage can be a preventive and monitoring way against COVID-19 in an operating unit with many patients and professionals, and other articles developed a telemedicine system, too, which allows patients to make scheduled visits without coming to the hospital and also for the staff, who can work remotely. In addition, 5 articles concerning psychological aspects of both patients and health care providers were included. Conclusion: This document can be used as a summary in the coming months/years, during the recovery phase from COVID-19 pandemic outbreak and as a starting point to be used in case of further pandemic break-out.

Personalised support of brain tumour patients during radiotherapy based on psychological profile and quality of life.

PURPOSE: Psychological distress in primary malignant brain tumour (PMBT) patients is associated with poorer outcomes. Radiotherapy (RT) often induces side effects that significantly influence patients' quality of life (QoL), with potential impact on survival. We evaluated distress, anxiety, depression, and QoL over time to identify patients with difficulties in these areas who required more intense psychological support. METHODS: Psychological questionnaires-Distress Thermometer (DT), Hospital Anxiety and Depression Scale (HADS), and Functional Assessment of Cancer Therapy (FACT-G and FACT-Br)-were completed at the beginning (T0), in the middle (T1), directly after RT (T2), and 3 months after RT (T3). We personalised the psychological support provided for each patient with a minimum of three sessions ('typical' schedule) and a maximum of eight sessions ('intensive' schedule), depending on the patients' psychological profiles, clinical evaluations, and requests. Patients' survival was evaluated in the glioblastoma multiforme (GBM) patients, with an explorative intent. RESULTS: Fifty-nine consecutive PMBT patients receiving post-operative RT were included. For patients who were reported as 'not distressed' at T0, no statistically significant changes were noted. In contrast, patients who were 'distressed' at T0 showed statistically significant improvements in DT, HADS, FACT-G, and FACT-Br scores over time. 'Not distressed' patients required less psychological sessions over the study duration than 'distressed' patients. Interestingly, 'not distressed' GBM patients survived longer than 'distressed' GBM patients. CONCLUSIONS: Increased psychological support improved distress, mood, and QoL for patients identified as 'distressed', whereas psychological well-being was maintained with typical psychological support in patients who were identified as being 'not distressed'. These results encourage a standardisation of psychological support for all RT patients.

A field strength independent MR radiomics model to predict pathological complete response in locally advanced rectal cancer.

PURPOSE: Aim of this study was to develop a generalised radiomics model for predicting pathological complete response after neoadjuvant chemo-radiotherapy in locally advanced rectal cancer patients using pre-CRT T2-weighted images acquired at a 1.5 T and a 3 T scanner. METHODS: In two institutions, 195 patients were scanned: 136 patients were scanned on a 1.5 T MR scanner, 59 patients on a 3 T MR scanner. Gross tumour volumes were delineated on the MR images and 496 radiomic features were extracted, applying the intensity-based (IB) filter. Features were standardised with Z-score normalisation and an initial feature selection was carried out using Wilcoxon-Mann-Whitney test: The most significant features at 1.5 T and 3 T were selected as main features. Several logistic regression models combining the main features with a third one selected by those resulting significant were elaborated and evaluated in terms of area under curve (AUC). A tenfold cross-validation was repeated 300 times to evaluate the model robustness. RESULTS: Three features were selected: maximum fractal dimension with IB = 0-50, energy and grey-level non-uniformity calculated on the run-length matrix with IB = 0-50. The AUC of the model applied to the whole dataset after cross-validation was 0.72, while values of 0.70 and 0.83 were obtained when 1.5 T and 3 T patients were considered, respectively. CONCLUSIONS: The model elaborated showed good performance, even when data from patients scanned on 1.5 T and 3 T were merged. This shows that magnetic field intensity variability can be overcome by means of selecting appropriate image features.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping.

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy.

PURPOSE: MR-guided radiotherapy (MRgRT) relies on the daily assignment of a relative electron density (RED) map to allow the fraction specific dose calculation. One approach to assign the RED map consists of segmenting the daily magnetic resonance image into five different density levels and assigning a RED bulk value to each level to generate a synthetic CT (sCT). The aim of this study is to evaluate the dose calculation accuracy of this approach for applications in MRgRT. METHODS: A planning CT (pCT) was acquired for 26 patients with abdominal and pelvic lesions and segmented in five levels similar to an online approach: air, lung, fat, soft tissue and bone. For each patient, the median RED value was calculated for fat, soft tissue and bone. Two sCTs were generated assigning different bulk values to the segmented levels on pCT: The sCTICRU uses the RED values recommended by ICRU46, and the sCTtailor uses the median patient-specific RED values. The same treatment plan was calculated on two the sCTs and the pCT. The dose calculation accuracy was investigated in terms of gamma analysis and dose volume histogram parameters. RESULTS: Good agreement was found between dose calculated on sCTs and pCT (gamma passing rate 1%/1 mm equal to 91.2% ± 6.9% for sCTICRU and 93.7% ± 5.3% b or sCTtailor). The mean difference in estimating V95 (PTV) was equal to 0.2% using sCTtailor and 1.2% using sCTICRU, respect to pCT values CONCLUSIONS: The bulk sCT guarantees a high level of dose calculation accuracy also in presence of magnetic field, making this approach suitable to MRgRT. This accuracy can be improved by using patient-specific RED values.

A new frontier of image guidance: Organs at risk avoidance with MRI-guided respiratory-gated intensity modulated radiotherapy: Technical note and report of a case.

The case of a 50-year-old man affected by a rhabdomiosarcoma metastatic lesion in the left flank Is reported. The patient was addressed to 50.4 Gy radiotherapy with concomitant chemotherapy in order to locally control the lesion. A Tri-60-Co magnetic resonance hybrid radiotherapy unit was used for treatment delivery and a respiratory gating protocol was applied for the different breathing phases (Free Breathing, Deep Inspiration Breath Hold and Final Expiration Breath Hold). Three intensity modulated radiation therapy (IMRT) plans were calculated and Final Expiration Breath Hold plan was finally selected due to the absence of PTV coverage differences and better organs at risk sparing (i.e. kidneys). This case report suggests that organs at risk avoidance with MRI-guided respiratory-gated Radiotherapy is feasible and particularly advantageous whenever sparing the organs at risk is of utmost dosimetric or clinical importance.

Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study.

OBJECTIVES: Recently, radiomic analysis has gained attention as a valuable instrument for the management of oncological patients. The aim of the study is to isolate which features of magnetic resonance imaging (MRI)-based radiomic analysis have to be considered the most significant predictors of metastasis in oncological patients with spinal bone marrow metastatic disease. MATERIALS AND METHODS: Eight oncological patients (3 lung cancer; 1 prostatic cancer; 1 esophageal cancer; 1 nasopharyngeal cancer; 1 hepatocarcinoma; 1 breast cancer) with pre-radiotherapy MR imaging for a total of 58 dorsal vertebral bodies, 29 metastatic and 29 non-metastatic were included. Each vertebral body was contoured in T1 and T2 weighted images at a radiotherapy delineation console. The obtained data were transferred to an automated data extraction system for morphological, statistical and textural analysis. Eighty-nine features for each lesion in both T1 and T2 images were computed as the median of by-slice values. A Wilcoxon test was applied to the 89 features and the most statistically significant of them underwent to a stepwise feature selection, to find the best performing predictors of metastasis in a logistic regression model. An internal cross-validation via bootstrap was conducted for estimating the model performance in terms of the area under the curve (AUC) of the receiver operating characteristic. RESULTS: Of the 89 textural features tested, 16 were found to differ with statistical significance in the metastatic vs non-metastatic group. The best performing model was constituted by two predictors for T1 and T2 images, namely one morphological feature (center of mass shift) (p value < 0.01) for both datasets and one histogram feature minimum grey level (p value < 0.01) for T1 images and one textural feature (grey-level co-occurrence matrix joint variance (p value < 0.01) for T2 images. The internal cross-validation showed an AUC of 0.8141 (95% CI 0.6854-0.9427) in T1 images and 0.9116 (95% CI 0.8294-0.9937) in T2 images. CONCLUSIONS: The results suggest that MRI-based radiomic analysis on oncological patients with bone marrow metastatic disease is able to differentiate between metastatic and non-metastatic vertebral bodies. The most significant predictors of metastasis were found to be based on T2 sequence and were one morphological and one textural feature.

Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach.

The aim of this study was to evaluate the variation of radiomics features, defined as "delta radiomics", in patients undergoing neoadjuvant radiochemotherapy (RCT) for rectal cancer treated with hybrid magnetic resonance (MR)-guided radiotherapy (MRgRT). The delta radiomics features were then correlated with clinical complete response (cCR) outcome, to investigate their predictive power. A total of 16 patients were enrolled, and 5 patients (31%) showed cCR at restaging examinations. T2*/T1 MR images acquired with a hybrid 0.35 T MRgRT unit were considered for this analysis. An imaging acquisition protocol of 6 MR scans per patient was performed: the first MR was acquired at first simulation (t0) and the remaining ones at fractions 5, 10, 15, 20 and 25. Radiomics features were extracted from the gross tumour volume (GTV), and each feature was correlated with the corresponding delivered dose. The variations of each feature during treatment were quantified, and the ratio between the values calculated at different dose levels and the one extracted at t0 was calculated too. The Wilcoxon-Mann-Whitney test was performed to identify the features whose variation can be predictive of cCR, assessed with a MR acquired 6 weeks after RCT and digital examination. The most predictive feature ratios in cCR prediction were the L_least and glnu ones, calculated at the second week of treatment (22 Gy) with a p value = 0.001. Delta radiomics approach showed promising results and the quantitative analysis of images throughout MRgRT treatment can successfully predict cCR offering an innovative personalized medicine approach to rectal cancer treatment.

Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.

The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario "Agostino Gemelli" of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.

Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.

BACKGROUND: To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma. METHODS: A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis. RESULTS: The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585-0.7732; model-2: GLNU p < 0.05, AUC 0.6396, C.I.:0.5266-0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232-0.8797)). CONCLUSION: A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.

Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.

Early changes of parotid density and volume predict modifications at the end of therapy and intensity of acute xerostomia.

PURPOSE: To quantitatively assess the predictive power of early variations of parotid gland volume and density on final changes at the end of therapy and, possibly, on acute xerostomia during IMRT for head-neck cancer. MATERIALS AND METHODS: Data of 92 parotids (46 patients) were available. Kinetics of the changes during treatment were described by the daily rate of density (rΔρ) and volume (rΔvol) variation based on weekly diagnostic kVCT images. Correlation between early and final changes was investigated as well as the correlation with prospective toxicity data (CTCAEv3.0) collected weekly during treatment for 24/46 patients. RESULTS: A higher rΔρ was observed during the first compared to last week of treatment (-0,50 vs -0,05HU, p-value = 0.0001). Based on early variations, a good estimation of the final changes may be obtained (Δρ: AUC = 0.82, p = 0.0001; Δvol: AUC = 0.77, p = 0.0001). Both early rΔρ and rΔvol predict a higher "mean" acute xerostomia score (≥ median value, 1.57; p-value = 0.01). Median early density rate changes for patients with mean xerostomia score ≥ / < 1.57 were -0.98 vs -0.22 HU/day respectively (p = 0.05). CONCLUSIONS: Early density and volume variations accurately predict final changes of parotid glands. A higher longitudinally assessed score of acute xerostomia is well predicted by higher rΔρ and rΔvol in the first two weeks of treatment: best cut-off values were -0.50 HU/day and -380 mm(3)/day for rΔρ and rΔvol respectively. Further studies are necessary to definitively assess the potential of early density/volume changes in identifying more sensitive patients at higher risk of experiencing xerostomia.

Psychological Experiences of Parents of Pediatric Cancer Patients during and after COVID-19 Pandemic.

BACKGROUND: Family members dealing with the devastating impact of a cancer diagnosis are now facing even greater vulnerability due to the COVID-19 pandemic. Alongside the already overwhelming trauma, they must also bear the distressing burden of the infection risks. The purpose of this study was to examine and explore the effects in parents of pediatric cancer patients two years after the start of the COVID-19 pandemic to compare these data with the previous data. METHODS: We conducted a single-center prospective observational study, enrolling 75 parents of 42 pediatric oncology patients. Four questionnaires (IES-R; PSS; STAI-Y and PedsQL) were given to the parents 2 years after the first evaluation. RESULTS: The bivariate matrix of correlation found a strong significant positive correlation between IES-R and PSS scores (r = 0.526, p < 0.001) as in T1. Stress symptoms (t = 0.00, p < 0.001) and levels of anxiety (trait) (t = 0.32, p < 0.001) remained unchanged; anxiety state levels appeared to have increased (t = 0.425, p < 0.001); there was a significant decrease in the PedsQL tot (t = 5.25, p < 0.001). CONCLUSIONS: The COVID-19 pandemic has influenced the levels of stress and anxiety of parents and the quality of life of patients, also correlating with the traumatic impact of the diagnosis.

Clinical validation of atlas-based auto-segmentation of pelvic volumes and normal tissue in rectal tumors using auto-segmentation computed system.

PURPOSE: To evaluate in two different settings - clinical practice and education/training - the reliability, time efficiency and the ideal sequence of an atlas-based auto-segmentation system in pelvic delineation of locally advanced rectal cancer. METHODS: Fourteen consecutive patients were selected between October and December 2011. The images of four were used as an atlas and 10 used for validation. Two independent operators participated: a Delineator to contour and a Reviewer to perform an independent check (IC). The CTV, pelvic subsites and organs at risk were contoured in four different sequences. These included A: manual; B: auto-segmentation; C: auto-segmentation + manual revision; and D: manual + auto-segmentation + manual revision. Contouring was performed by the Delineator using the same planning CT. All of them underwent an IC by a Reviewer. The time required for all the contours were recorded and overlapping evaluation was assessed using a Dice coefficient. RESULTS: In the clinical practice setting there have been 13 minutes time saved between sequences A versus sequences B (from 38 to 25 minutes, p = 0.002), a mean Dice coefficient in favor of sequences A for CTV and all subsites (p = 0.0195). In the educational/training setting there have been 35.2 minutes time saved between sequences C and D 8 (from 73.1 min to 37.9 min, p = 0.002). CONCLUSION: The preliminary data suggest that the use of an atlas-based auto-contouring system may help improve efficiencies in contouring in the clinical practice setting and could have a tutorial role in the educational/training setting.

May we routinely spare hippocampal region in primary central nervous system lymphoma during whole brain radiotherapy?

PURPOSE: One of the main limiting factors of whole-brain radiation therapy (WBRT) for primary central nervous system lymphoma (PCNSL) is the impairment of neurocognitive functions (NCFs), which is mainly caused by radiation-induced injury to the hippocampus. With a view to preventing NCF impairment and personalizing treatment, we explored the feasibility of sparing the hippocampus during WBRT by correlating the sites of PCNSL lesions with the hippocampus. METHODS AND MATERIALS: Pre-treatment MR images from patients who underwent WBRT between 2010 and January 2020-and post-radiotherapy images in cases of relapse-were imported into the Varian Eclipse treatment-planning system and registered with the simulation CT. We constructed three 3-dimensional envelopes around the hippocampus at distances of 5, 10 and 15 mm and also contoured primary lesions and recurrences. RESULTS: We analyzed 43 patients with 66 primary lesions: 9/66 (13.6%) involved the hippocampus and 11/66 (16.7%) were located within 5 mm of it. Thirty-six lesions (54.5%) were situated more than 15 mm from the hippocampus, while 10/66 (15.2%) were between 5 and 15 mm from it. The most common location was in deep brain structures (31%). Thirty-five of the 66 lesions relapsed: in field in 14/35 (40%) and outfield in 21/35 (60%) in different sites. Globally, 16/35 recurrences (45.7%) were located in the hippocampus or within 5 mm of it. CONCLUSION: These data show that routinely sparing the hippocampus is not feasible. This approach could be considered in selected patients, when the lesion is more than 15 mm from the hippocampus.