Faculty Bibliography

AIM:Ga]Ga-Pentixafor uptake in atherosclerotic plaques of the carotid arteries, and the impact of ignoring bone in MR-based attenuation correction (MR-AC). METHODS:Ga]Ga-Pentixafor uptake were compared for standard and PVC-PET images. A potential influence of ignoring bone in MR-AC was assessed in a subset of the data reconstructed after incorporating bone into MR-AC and a subsequent comparison of standardized-uptake values (SUV). RESULTS:In total, 34 atherosclerotic plaques were identified. Following PVC, mean and max TBR increased by 77 and 95%, respectively, when averaged across lesions. When accounting for bone in the MR-AC, SUV of plaque changed by 0.5%. CONCLUSION:Ga]Ga-pentixafor uptake in plaques are strongly affected by PVE, which can be reduced by PVC. Including bone information into the MR-AC yielded no clinically relevant effect on tracer quantification.

Personalized medicine aims to match the right drug with the right patient by using specific features of the individual patient's tumor. However, current strategies of personalized therapy matching provide treatment opportunities for less than 10% of patients with cancer. A promising method may be drug profiling of patient biopsy specimens with single-cell resolution to directly quantify drug effects. We prospectively tested an image-based single-cell functional precision medicine (scFPM) approach to guide treatments in 143 patients with advanced aggressive hematologic cancers. Fifty-six patients (39%) were treated according to scFPM results. At a median follow-up of 23.9 months, 30 patients (54%) demonstrated a clinical benefit of more than 1.3-fold enhanced progression-free survival compared with their previous therapy. Twelve patients (40% of responders) experienced exceptional responses lasting three times longer than expected for their respective disease. We conclude that therapy matching by scFPM is clinically feasible and effective in advanced aggressive hematologic cancers. SIGNIFICANCE: This is the first precision medicine trial using a functional assay to instruct n-of-one therapies in oncology. It illustrates that for patients lacking standard therapies, high-content assay-based scFPM can have a significant value in clinical therapy guidance based on functional dependencies of each patient's cancer.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275.

Posttreatment evaluation of gastric mucosa-associated lymphoid tissue (MALT) lymphoma currently relies on esophagogastroduodenoscopy with histological assessment of biopsies. Overexpression of the G protein-coupled C-X-C chemokine receptor type 4 (CXCR4) has been previously observed in MALT lymphoma. The aim of this prospective study was to evaluate positron emission tomography (PET) with the novel CXCR4 tracer [68Ga]Pentixafor as a potential alternative to follow up biopsies for assessment of residual disease (noncomplete remission [CR]) after first-line Helicobacter pylori eradication. Forty-six post-H pylori eradication [68Ga]Pentixafor-PET/magnetic resonance imaging (MRI) examinations of 26 gastric MALT lymphoma patients, and 20 [68Ga]Pentixafor-PET/MRI examinations of 20 control group patients without lymphoma, were analyzed. In the MALT lymphoma group, time-matched gastric biopsies were used as reference standard and showed CR in 6 cases. Pooled examination-based accuracy, sensitivity, specificity, and positive and negative predictive values of [68Ga]Pentixafor-PET for detection of residual gastric MALT lymphoma at follow-up were 97.0%, 95.0%, 100.0%, 100.0%, and 92.9%, respectively. Maximum and mean PET standardized uptake values showed moderate correlation with immunohistochemistry-based CXCR4+ cell counts, with correlation coefficients of r = 0.51 and r = 0.52 (P = .008 and P = .006). In summary, CXCR4 imaging with [68Ga]Pentixafor-PET may represent a promising test for assessment of residual gastric MALT lymphomas after H pylori eradication.

PURPOSE:F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. METHODS:F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. RESULTS:F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. CONCLUSION:F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective.

OBJECTIVES:To explore the potential of Radiomics alone and in combination with a diffusion-weighted derived quantitative parameter, namely the apparent diffusion co-efficient (ADC), using supervised classification algorithms in the prediction of outcomes and prognosis. MATERIALS AND METHODS:Retrospective evaluation of the imaging was conducted for a study cohort of uterine cervical cancer, candidates for radical treatment with chemo radiation. ADC values were calculated from the darkest part of the tumor, both before (labeled preADC) and post treatment (labeled postADC) with chemo radiation. Post extraction of 851 Radiomics features and feature selection analysis-by taking the union of the features that had Pearson correlation >0.35 for recurrence, >0.49 for lymph node and >0.40 for metastasis-was performed to predict clinical outcomes. RESULTS:The study enrolled 52 patients who presented with variable FIGO stages in the age range of 28-79 (Median = 53 years) with a median follow-up of 26.5 months (range: 7-76 months). Disease recurrence occurred in 12 patients (23%). Metastasis occurred in 15 patients (28%). A model generated with 24 radiomics features and preADC using a monotone multi-layer perceptron neural network to predict the recurrence yields an AUC of 0.80 and a Kappa value of 0.55 and shows that the addition of radiomics features to ADC values improves the statistical metrics by approximately 40% for AUC and approximately 223% for Kappa. Similarly, the neural network model for prediction of metastasis returns an AUC value of 0.84 and a Kappa value of 0.65, thus exceeding performance expectations by approximately 25% for AUC and approximately 140% for Kappa. There was a significant input of GLSZM features (SALGLE and LGLZE) and GLDM features (SDLGLE and DE) in correlation with clinical outcomes of recurrence and metastasis. CONCLUSIONS:The study is an effort to bridge the unmet need of translational predictive biomarkers in the stratification of uterine cervical cancer patients based on prognosis.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Vaccination-associated adenopathy is a frequent imaging finding after administration of COVID-19 vaccines that may lead to a diagnostic conundrum in patients with manifest or suspected cancer, in whom it may be indistinguishable from malignant nodal involvement. To help the medical community address this concern in the absence of studies and evidence-based guidelines, this special report offers recommendations developed by a multidisciplinary panel of experts from three of the leading tertiary care cancer centers in the United States. According to these recommendations, some routine imaging examinations, such as those for screening, should be scheduled before or at least 6 weeks after the final vaccination dose to allow for any reactive adenopathy to resolve. However, there should be no delay of other clinically indicated imaging (eg, for acute symptoms, short-interval treatment monitoring, urgent treatment planning or complications) due to prior vaccination. The vaccine should be administered on the side contralateral to the primary or suspected cancer, and both doses should be administered in the same arm. Vaccination information-date(s) administered, injection site(s), laterality, and type of vaccine-should be included in every preimaging patient questionnaire, and this information should be made readily available to interpreting radiologists. Clear and effective communication between patients, radiologists, referring physician teams, and the general public should be considered of the highest priority when managing adenopathy in the setting of COVID-19 vaccination.

OBJECTIVES/OBJECTIVE:To evaluate the performance of radiomic features extracted from high-resolution computed tomography (HRCT) for the differentiation between cholesteatoma and middle ear inflammation (MEI), and to investigate the impact of post-reconstruction harmonization and data resampling. METHODS:One hundred patients were included in this retrospective dual-center study: 48 with histology-proven cholesteatoma (center A: 23; center B: 25) and 52 with MEI (A: 27; B: 25). Radiomic features (co-occurrence and run-length matrix, absolute gradient, autoregressive model, Haar wavelet transform) were extracted from manually defined 2D-ROIs. The ten best features for lesion differentiation were selected using probability of error and average correlation coefficients. A multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used for radiomics-based classification, with histopathology serving as the reference standard (70% of cases for training, 30% for validation). The analysis was performed five times each on (a) unmodified data and on data that were (b) resampled to the same matrix size, and (c) corrected for acquisition protocol differences using ComBat harmonization. RESULTS:Using unmodified data, the MLP-ANN classification yielded an overall median area under the receiver operating characteristic curve (AUC) of 0.78 (0.72-0.84). Using original data from center A and resampled data from center B, an overall median AUC of 0.88 (0.82-0.99) was yielded, while using ComBat harmonized data, an overall median AUC of 0.89 (0.79-0.92) was revealed. CONCLUSION/CONCLUSIONS:Radiomic features extracted from HRCT differentiate between cholesteatoma and MEI. When using multi-centric data obtained with differences in CT acquisition parameters, data resampling and ComBat post-reconstruction harmonization clearly improve radiomics-based lesion classification. KEY POINTS/CONCLUSIONS:• Unenhanced high-resolution CT coupled with radiomics analysis may be useful for the differentiation between cholesteatoma and middle ear inflammation. • Pooling of data extracted from inhomogeneous CT datasets does not appear meaningful without further post-processing. • When using multi-centric CT data obtained with differences in acquisition parameters, post-reconstruction harmonization and data resampling clearly improve radiomics-based soft-tissue differentiation.