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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]

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 riskmanagement 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]

Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. Critical relevance statement: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. Key Points: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability. [ABSTRACT FROM AUTHOR]

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]

Objective: We studied the repeatability and the relative intra-scan variability across acquisition protocols in CT using phantom and unenhanced abdominal series. Methods: We used 17 CT scans from the Credence Cartridge Radiomics Phantom database and 20 unenhanced multi-site non-pathologic abdominal patient series for which we measured spleen and liver tissues. We performed multiple measurements in extracting 9 radiomics features. We defined a "tandem" as the measurement of a given tissue (or material) by a given radiomics. For each tandem, we assessed the proportion of the variability attributable to repetitions, acquisition protocols, material, or patient. We analyzed the distribution of the intra-scan correlation between pairs of tandems and checked the impact of correlation coefficient greater than 0.90 in comparing paired and unpaired differences. Results: The repeatability of radiomics features depends on the measured material; 56% of tandems were highly repeatable. Histogram-derived radiomics were generally less repeatable. Nearly 60% of relative radiomics measurements had a correlation coefficient higher than 0.90 allowing paired measurements to improve reliability in detecting the difference between two materials. The analysis of liver and spleen tissues showed that measurement variability was negligible with respect to other variabilities. As for phantom data, we found that gray level zone length matrix (GLZLM)–derived radiomics and gray level co-occurrence matrix (GLCM)–derived radiomics were the most correlating features. For these features, relative intra-scan measurements improved the detection of different materials or tissues. Conclusions: We identified radiomics features for which the intra-scan measurements between tissues are linearly correlated. This property represents an opportunity to improve tissue characterization and inter-site harmonization. Key Points: • The repeatability of radiomics features on CT depends on the measured material or tissue. • Some tandems of radiomics features/tissues are linearly affected by the variability of acquisition protocols on CT. • Relative intra-scan measurements are an opportunity for improving quantitative imaging on CT. [ABSTRACT FROM AUTHOR]

Objectives: Following the craze for radiomic features (RF), their lack of reliability raised the question of the generalizability of classification models. Inter-site harmonization of images therefore becomes a central issue. We compared RF harmonization processing designed to detect liver diseases in CT images. Methods: We retrospectively analyzed 76 multi-center portal CT series of non-diseased (NDL) and diseased liver (DL) patients. In each series, we positioned volumes of interest in spleen and liver, then extracted 9 RF (histogram and texture). We evaluated two RF harmonization approaches. First, in each series, we computed the Z-score of liver measurements based on those computed in the spleen. Second, we evaluated the ComBat method according to each imaging center; parameters were computed in the spleen and applied to the liver. We compared RF distributions and classification performances before/after harmonization. We classified NDL versus spleen and versus DL tissues. Results: The RF distributions were all different between liver and spleen (p < 0.05). The Z-score harmonization outperformed for the detection of liver versus spleen: AUC = 93.1% (p < 0.001). For the detection of DL versus NDL, in a case/control setting, we found no differences between the harmonizations: mean AUC = 73.6% (p = 0.49). Using the whole datasets, the performances were improved using ComBat (p = 0.05) AUC = 82.4% and degraded with Z-score AUC = 67.4% (p = 0.008). Conclusions: Data harmonization requires to first focus on data structuring to not degrade the performances of subsequent classifications. Liver tissue classification after harmonization of spleen-based RF is a promising strategy for improving the detection of DL tissue. Key Points: • Variability of acquisition parameter makes radiomics of CT features non-reproducible. • Data harmonization can help circumvent the inter-site variability of acquisition protocols. • Inter-site harmonization must be carefully implemented and requires designing consistent data sets. [ABSTRACT FROM AUTHOR]

Response Evaluation Criteria In Solid Tumors (RECIST) is still the predominant criteria base for assessing tumor burden in oncology clinical trials. Despite several improvements that followed its first publication, RECIST continues to allow readers a lot of freedom in their evaluations. Notably in the selection of tumors at baseline. This subjectivity is the source of many suboptimal evaluations. When starting a baseline analysis, radiologists cannot always identify tumor malignancy with any certainty. Also, with RECIST, some findings can be deemed equivocal by radiologists with no confirmatory ground truth to rely on. In the specific case of Blinded Independent Central Review clinical trials with double reads using RECIST, the selection of equivocal tumors can have two major consequences: inter-reader variability and modified sensitivity of the therapeutic response. Apart from the main causes leading to the selection of an equivocal lesion, due to the uncertainty of the radiological characteristics or due to the censoring of on-site evaluations, several other situations can be described more precisely. These latter involve cases where an equivocal is selected as target or non-target lesions, the management of equivocal lymph nodes and the case of few target lesions. In all cases, awareness of the impact of selecting a non-malignant lesion will lead radiologists to make selections in the most rational way. Also, in clinical trials where the primary endpoint differs between phase 2 (response-related) and phase 3 (progression-related) trials, our impact analysis will help them to devise strategies for the management of equivocal lesions. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are booming in medicine, and the question of biases induced or perpetuated by these tools is a very important topic. There is a greater risk of these biases in radiology, which is now the primary diagnostic tool in modern treatment. Some authors have recently proposed an analysis framework for social inequalities and the biases at risk of being introduced into future algorithms. In our paper, we comment on the different strategies for resolving these biases. We warn that there is an even greater risk in mixing the notion of equity, the definition of which is socio-political, into the design stages of these algorithms. We believe that rather than being beneficial, this could in fact harm the main purpose of these artificial intelligence tools, which is the care of the patient. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms are booming in medicine, and the question of biases induced or perpetuated by these tools is a very important topic. There is a greater risk of these biases in radiology, which is now the primary diagnostic tool in modern treatment. Some authors have recently proposed an analysis framework for social inequalities and the biases at risk of being introduced into future algorithms. In our paper, we comment on the different strategies for resolving these biases. We warn that there is an even greater risk in mixing the notion of equity, the definition of which is socio-political, into the design stages of these algorithms. We believe that rather than being beneficial, this could in fact harm the main purpose of these artificial intelligence tools, which is the care of the patient. [ABSTRACT FROM AUTHOR]

Objective: To assess the reliability of ADC measurements in vitro and in cervical lymph nodes of healthy volunteers.Methods: We used a GE 1.5 T MRI scanner and a first ice-water phantom according to recommendations released by the Quantitative Imaging Biomarker Alliance (QIBA) for assessing ADC against reference values. We analysed the target size effect by using a second phantom made of six inserted spheres with diameters ranging from 10 to 37 mm. Thirteen healthy volunteers were also scanned to assess the inter- and intra-observer reproducibility of volumetric ADC measurements of cervical lymph nodes.Results: On the ice-water phantom, the error in ADC measurements was less than 4.3 %. The spatial bias due to the non-linearity of gradient fields was found to be 24 % at 8 cm from the isocentre. ADC measure reliability decreased when addressing small targets due to partial volume effects (up to 12.8 %). The mean ADC value of cervical lymph nodes was 0.87.10-3 ± 0.12.10-3 mm2/s with a good intra-observer reliability. Inter-observer reproducibility featured a bias of -5.5 % due to segmentation issues.Conclusion: ADC is a potentially important imaging biomarker in oncology; however, variability issues preclude its broader adoption. Reliable use of ADC requires technical advances and systematic quality control.Key Points: • ADC is a promising quantitative imaging biomarker. • ADC has a fair inter-reader variability and good intra-reader variability. • Partial volume effect, post-processing software and non-linearity of scanners are limiting factors. • No threshold values for detecting cervical lymph node malignancy can be drawn. [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.

The original article can be found online at https://doi.org/10.1007/s00330-021-08154-8. B Correction to: European Radiology b https://doi.org/10.1007/s00330-021-08154-8 The original version of this article, published on 06 August 2021, unfortunately contained a mistake. [Extracted from the article]

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]

Background: To evaluate the effectiveness and feasibility of high-intensity focused ultrasound (HIFU) for the treatment of bone metastases. Methods: A single-center prospective study was made involving 17 consecutive patients with symptomatic bone metastases. Patients were treated by Focused Ultrasound (FUs) performed with magnetic resonance (MR) guidance. Surgical treatment or radiotherapy treatment was not indicated for patients who underwent FUs. Lesions were located in the appendicular and axial skeleton and consisted of secondary symptomatic lesions. The clinical course of pain was evaluated using the Visual Analog Scale (VAS) before treatment, at 1 week, and at 1 month after treatment and the Oral Morphine Equivalent Daily Dose (OMEDD) was also recorded. We used Wilcoxon signed rank test to assess change in patient pain (R CRAN software V 3.1.1). Results: We observed a significant decrease in the pain felt by patients between pre- procedure and 1 week post-procedure (p = 2.9.10–4), and pre-procedure and 1 month post-procedure (p = 3.10–4). The proportion of responders according to the International Bone Metastases Consensus Working Party was: Partial Response 50% (8/16) and Complete Response 37.5% (6/16). Conclusions: HIFU under MR-guidance seems to be an effective and safe procedure in the treatment of symptomatic bone lesions for patients suffering from metastatic disease. A significant decrease of patient pain was observed. Trial registration: NCT01091883. Registered 24 March 2010. Level of evidence: Level 3. [ABSTRACT FROM AUTHOR]