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Simple Summary: T cell receptor fusion constructs (TRuC-Ts) represent a promising next-generation T cell therapy for solid tumors. To enhance their development, improved patient selection is essential. A pilot study was conducted to evaluate the feasibility and performance of a predictive model for treatment responses in mesothelioma patients, leveraging radiomics and machine learning. Radiomics and delta-radiomics (Δradiomics) features from CT scans were analyzed for reproducibility and informativeness, identifying key features for training a random forest classifier. The model achieved an accuracy of 0.75–0.9 in predicting pleural tumor responses, supporting the design of future studies involving 250–400 tumors. This study demonstrated the reproducibility and effectiveness of radiomics/Δradiomics in relation to tumor localization, emphasizing the need for multiple tumor models to create an integrated patient model. These findings provide a foundation for combining tumor-specific models into a unified approach, improving patient selection for TRuC-T therapy in mesothelioma patients. Background/Objectives: T cell receptor fusion constructs (TRuCs), a next generation engineered T cell therapy, hold great promise. To accelerate the clinical development of these therapies, improving patient selection is a crucial pathway forward. Methods: We retrospectively analyzed 23 mesothelioma patients (85 target tumors) treated in a phase 1/2 single arm clinical trial (NCT03907852). Five imaging sites were involved, the settings for the evaluations were Blinded Independent Central Reviews (BICRs) with double reads. The reproducibility of 3416 radiomics and delta-radiomics (Δradiomics) was assessed. The univariate analysis evaluated correlations at the target tumor level with (1) tumor diameter response; (2) tumor volume response, according to the Quantitative Imaging Biomarker Alliance; and (3) the mean standard uptake value (SUV) response, as defined by the positron emission tomography response criteria in solid tumors (PERCISTs). A random forest model predicted the response of the target pleural tumors. Results: Tumor anatomical distribution was 55.3%, 17.6%, 14.1%, and 10.6% in the pleura, lymph nodes, peritoneum, and soft tissues, respectively. Radiomics/Δradiomics reproducibility differed across tumor localizations. Radiomics were more reproducible than Δradiomics. In the univariate analysis, none of the radiomics/Δradiomics correlated with any response criteria. With an accuracy ranging from 0.75 to 0.9, three radiomics/Δradiomics were able to predict the response of target pleural tumors. Pivotal studies will require a sample size of 250 to 400 tumors. Conclusions: The prediction of responding target pleural tumors can be achieved using a machine learning-based radiomics/Δradiomics analysis. Tumor-specific reproducibility and the average values indicated that using tumor models to create an effective patient model would require combining several target tumor models. [ABSTRACT FROM AUTHOR]

With the increasingly central role of imaging in medical diagnosis, understanding and monitoring radiological errors has become essential. In the field of oncology, the severity of the disease makes radiological error more visible, with both individual consequences and public health issues. The quantitative trend radiology allows to consider the diagnostic task as a problem of classification supported by the latest neurocognitive theories in explaining decision making errors, this purposeful model provides an actionable framework to support root cause analysis of diagnostic errors in radiology and envision corresponding risk-management strategies. The D for Data, A for Analysis and C for Communication are the three drivers of errors and we propose a practical toolbox for our colleagues to prevent individual and systemic sources of error. [ABSTRACT FROM AUTHOR]

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

Background: In lung clinical trials with imaging, blinded independent central review with double reads is recommended to reduce evaluation bias and the Response Evaluation Criteria In Solid Tumor (RECIST) is still widely used. We retrospectively analyzed the inter-reader discrepancies rate over time, the risk factors for discrepancies related to baseline evaluations, and the potential of machine learning to predict inter-reader discrepancies. Materials and methods: We retrospectively analyzed five BICR clinical trials for patients on immunotherapy or targeted therapy for lung cancer. Double reads of 1724 patients involving 17 radiologists were performed using RECIST 1.1. We evaluated the rate of discrepancies over time according to four endpoints: progressive disease declared (PDD), date of progressive disease (DOPD), best overall response (BOR), and date of the first response (DOFR). Risk factors associated with discrepancies were analyzed, two predictive models were evaluated. Results: At the end of trials, the discrepancy rates between trials were not different. On average, the discrepancy rates were 21.0%, 41.0%, 28.8%, and 48.8% for PDD, DOPD, BOR, and DOFR, respectively. Over time, the discrepancy rate was higher for DOFR than DOPD, and the rates increased as the trial progressed, even after accrual was completed. It was rare for readers to not find any disease, for less than 7% of patients, at least one reader selected non-measurable disease only (NTL). Often the readers selected some of their target lesions (TLs) and NTLs in different organs, with ranges of 36.0-57.9% and 60.5-73.5% of patients, respectively. Rarely (4-8.1%) two readers selected all their TLs in different locations. Significant risk factors were different depending on the endpoint and the trial being considered. Prediction had a poor performance but the positive predictive value was higher than 80%. The best classification was obtained with BOR. Conclusion: Predicting discordance rates necessitates having knowledge of patient accrual, patient survival, and the probability of discordances over time. In lung cancer trials, although risk factors for inter-reader discrepancies are known, they are weakly significant, the ability to predict discrepancies from baseline data is limited. To boost prediction accuracy, it would be necessary to enhance baseline-derived features or create new ones, considering other risk factors and looking into optimal reader associations. [ABSTRACT FROM AUTHOR]

Background: In clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials. Material and methods: We retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated =50 patients in =two trials, to compare individual reader's selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively. Results: Across trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p<0.001). In intertrials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p<0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p<0.05). Conclusions: We found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design. [ABSTRACT FROM AUTHOR]

Objective: We compared therapeutic response of Varlitinib + Capecitabine (VC) versus Lapatinib + Capecitabine (LC) in patients with human epidermal growth factor receptor 2‐positive metastatic breast cancer after trastuzumab therapy by assessing changes in target lesion (TL) diameter and volume per location. Methods: We retrospectively analyzed the CT data of the ASLAN001‐003 study (NCT02338245). We analyzed TL size and number at each location focusing on therapeutic response from baseline to Week 12. We used TL diameter and volume to conduct an inter‐arm comparison of the response according to: RECIST 1.1; stratified per TL location and considering TLs independently. Multiple pairwise intra‐arm comparisons of therapeutic responses were performed. Considering TL independently, weighted models were designed by adding weighted mean TL responses grouped by location. Results: We evaluated 42 patients (88 TL) and 35 patients (74 TL), respectively, at baseline and Week 12. We found reductions in breast TL burden in the VC arm compared to the LC arm (p = 0.002 (diameter), p < 0.001 (volume)). Responses and TL sizes at baseline were not correlated. Explained variabilities of volume change per TL location, patient and patient:TL interaction were 36%, 10% and 4% (VC), and 13%, 1% and 23%, (LC). A test of inter‐arm difference of responses yielded p = 0.07 (diameter), and p < 0.001 (volume). Conclusions: The therapeutic responses differed across tumors' locations; the magnitude of the differences of responses across the tumors' locations were drug‐dependent. Stratified analysis of the response by tumor location improved drug comparisons and is a powerful tool to understand TL heterogeneity. [ABSTRACT FROM AUTHOR]

Purpose: In oncology clinical trials, nonconformity issues are frequently reported. Radiological workload is increasing, thus reducing radiologists' availability and affecting diagnostic quality. We compared performances of a standard radiological workflow (SW) and a novel "hybrid workflow" (HW).Method: We prospectively studied imaging data of 40 patients included in RECIST 1.1 clinical trials. Ninety-six time-points were reviewed by 7 radiologists and one trained technologist. Nonconformities using the SW were retrieved from hospital archives. For the HW, radiologists performed all baseline evaluations; the technologist made subsequent measurements. Finally, the radiologists checked the technologist's findings before confirming the evaluations. The HW enabled implementation of an electronic reporting system. An independent body compared SW and HW reading times and nonconformity occurrences.Results: Using SW, 19 types of nonconformity were found: blank report (13%); unsigned report (11%); undocumented change of tumor burden (10%); undocumented new lesions (9%); missing/wrong patients' appointment dates (7%); undocumented tumor location (5%); error in tumor burden change (5%). SW and HW nonconformities affected 55% (179/323) and 5% (2/40) of reports, respectively (p < 0.001). HW nonconformities were: one inaccurate login name was used on the platform, and one erroneous time-point number. On average, SW required 11'30″ [10'06″; 13'20″] per time-point. HW required 1'35″ [40″; 5'08″] for radiologists, and 12'18″ [11'12″; 14'18″] for the technologist.Conclusions: HW significantly reduced the number of trial nonconformities and saved 87% of radiologists' time while enabling them to apply their expertise to final decisions. HW could offer an effective opportunity for cost reduction associated with improved imaging trial quality. [ABSTRACT FROM AUTHOR]

Objective: The measure of body surface area (BSA) is a standard for planning optimal dosing in oncology. This index is derived from a model having questionable performances. In this study, we proposed measurement of BSA from whole body CT images (iBSA). We tested the reliability of iBSA assessments and simulated the impact of our approach on patient chemotherapy dosage planning. Methods: We first evaluated accuracy and precision of iBSA in measuring 14 phantom and 11 CT test-retest images.Secondly, we retrospectively analyzed 26 whole body PET-CT scans to evaluate inter-method variability between iBSA and the most used anthropomorphic models, notably the “Du Bois and Du Bois” model. Finally, we simulated the impact on chemotherapy dose planning of capecitabine based on iBSA. Results: Precision and accuracy of iBSA measurement featured a standard deviation of 1.11% and a mean error of 1.53%. Inter-method variability between iBSA and “Du Bois and Du Bois” assessment featured a standard deviation of 4.11% leading to a reclassification rate of capecitabine of 32.5%. Conclusions: iBSA could help the oncologist in standardizing assessments for chemotherapy planning. iBSA could also be relevant for applications such as comprehensive body composition and provide a sensitive measurement for changes related to nutritional intake or other metabolism. [ABSTRACT FROM AUTHOR]

The article discusses the characteristics of people with mental super-efficiency, high potential or atypical profile. Also cited are the percentage of the population comprising these gifted individuals, the move by J. S. Renzulli in 1978 to present three clusters of giftedness like high level of creativity and above average general ability, and the Theory of Multiple Intelligencies conceptualized by H. Gardner in 1987.

Simple Summary: Lung cancer treatment has dramatically evolved in the past decade, but some pitfalls of image interpretation have been introduced in parallel, such as pseudo-progressions. These challenges could be made more evident with blinded independent central reviews, as readers are often blinded to patient clinical symptoms and outcomes. The aim of this study was to analyze a pool of lung trials that used RECIST 1.1, document the proportion of reader discrepancies and the reader performance through monitoring procedures, and provide suggestions for the reduction of read inconsistency. This study provides benchmarks for the reader discordance rate in novel lung cancer therapeutic trials that will help to trigger corrective actions such as initial reader training and follow-up re-training. Background: Double reads in blinded independent central reviews (BICRs) are recommended to control the quality of trials but they are prone to discordances. We analyzed inter-reader discordances in a pool of lung cancer trials using RECIST 1.1. Methods: We analyzed six lung cancer BICR trials that included 1833 patients (10,684 time points) involving 17 radiologists. We analyzed the rate of discrepancy of each trial at the time-point and patient levels as well as testing inter-trial differences. The analysis of adjudication made it possible to compute the readers' endorsement rates, the root causes of adjudications, and the proportions of "errors" versus "medically justifiable differences". Results: The trials had significantly different discrepancy rates both at the time-point (average = 34.3%) and patient (average = 59.2%) levels. When considering only discrepancies for progressive disease, homogeneous discrepancy rates were found with an average of 32.9%, while readers' endorsement rates ranged between 27.7% and 77.8%. Major causes of adjudication were different per trial, with medically justifiable differences being the most common, triggering 74.2% of total adjudications. Conclusions: We provide baseline performances for monitoring reader performance in trials with double reads. Intelligent reading system implementation along with appropriate reader training and monitoring are solutions that could mitigate a large portion of the commonly encountered reading errors. [ABSTRACT FROM AUTHOR]

Purpose: Gaucher disease (GD) is an inherited lysosomal storage disorder. The Vertebral Disk Ratio (VDR) is a semi-quantitative imaging biomarker designed to diagnose and monitor GD. Computed from standard T1 MRI images, the VDR is derived from 2D segmentations. This study aimed to evaluate the 3D version of VDR, namely eVDR, and analyze the performances of two eVDR-derived response criteria for GD patients.Methods: Three datasets were used: 8 longitudinal GD patients, 13 non-GD patients, and 2 longitudinal GD patients with known Bone Marrow Burden (BMB) scores. Two eVDR-derived response criteria were tested: 1) a parametric version (PeVDR) averaging all eVDR measures recorded for the 5 lumbar vertebrae; and 2) a non-parametric version (NPeVDR), considering all eVDR measures as independent and evaluating therapeutic response in a paired fashion. Analyses included assessment of reader variability in eVDR (3D) versus VDR (2D) and comparison with BMB response criteria.Results: The repeatability of eVDR (3D) versus VDR (2D) demonstrated no difference in mean values but a lower variance (p < 0.004). The PeVDR intra-reader variability had a standard deviation < 0.1 with a coefficient of variation < 5%; the inter-reader variability featured a Limit of Agreement < 5% and a Bias < 3%. Observational comparison of eVDR and BMB scoring and sensitivity indicated a correlation between PeVDR and BMB, with an improved sensitivity with the NPeVDR version.Conclusions: Based on a standard MRI sequence, the eVDR imaging biomarker and its derived response criteria improved GD assessments and could help assessing other bone marrow diseases. [ABSTRACT FROM AUTHOR]

Background: Interventional radiology procedures in cancer patients cause stress and anxiety. Our objective was to relate our experience in the use of sophrology techniques during interventional radiology procedures and evaluate the effects on patient's pain and anxiety., Methods: We present a prospective observational study on 60 consecutive patients who underwent interventional radiology procedures in a context of oncologic management from September 2017 to March 2018. Forty-two patients were asked if they wished to benefit from the sophrology and hypnosis techniques during their procedure. A control group was also made including 18 patients. Anxiety level and pain were evaluated using the visual analog scale (VAS) before and during procedures., Results: We observed a significant decrease in anxiety experienced by patients during interventional radiology procedures compared to before procedures in the sophrology group (P=3.318E-08), and a level of anxiety and pain during gestures inferior to that of the control group (P=2.035E-06 and 7.03E-05 respectively)., Conclusions: Relaxing therapies, such as sophrology and hypnosis, seems to be an interesting additional tool for the management of patients in interventional oncology, inducing a decrease of stress, pain, and anxiety in patients.