Post-treatment FDG PET-CT in head and neck carcinoma: comparative analysis of 4 qualitative interpretative criteria in a large patient cohort

There is no consensus regarding optimal interpretative criteria (IC) for Fluorine-18 fluorodeoxyglucose (FDG) Positron Emission Tomography – Computed Tomography (PET-CT) response assessment following (chemo)radiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC). The aim was to compare accuracy of IC (NI-RADS, Porceddu, Hopkins, Deauville) for predicting loco-regional control and progression free survival (PFS). All patients with histologically confirmed HNSCC treated at a specialist cancer centre with curative-intent non-surgical treatment who underwent baseline and response assessment FDG PET-CT between August 2008 and May 2017 were included. Metabolic response was assessed using 4 different IC harmonised into 4-point scales (complete response, indeterminate, partial response, progressive disease). IC performance metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy) were compared. Kaplan-Meier and Cox proportional hazards regression analyses were performed for survival analysis. 562 patients were included (397 oropharynx, 53 hypopharynx, 48 larynx, 64 other/unknown primary). 420 patients (75%) received CRT and 142 (25%) had radiotherapy alone. Median follow-up was 26 months (range 3–148). 156 patients (28%) progressed during follow-up. All IC were accurate for prediction of primary tumour (mean NPV 85.0% (84.6–85.3), PPV 85.0% (82.5–92.3), accuracy 84.9% (84.2–86.0)) and nodal outcome (mean NPV 85.6% (84.1–86.6), PPV 94.7% (93.8–95.1), accuracy 86.8% (85.6–88.0)). Number of indeterminate scores for NI-RADS, Porceddu, Deauville and Hopkins were 91, 25, 20, 13 and 55, 70, 18 and 3 for primary tumour and nodes respectively. PPV was significantly reduced for indeterminate uptake across all IC (mean PPV primary tumour 36%, nodes 48%). Survival analyses showed significant differences in PFS between response categories classified by each of the four IC (p <0.001). All four IC have similar diagnostic performance characteristics although Porceddu and Deauville scores offered the best trade off of minimising indeterminate outcomes whilst maintaining a high NPV

Can MR textural analysis improve the prediction of extracapsular nodal spread in patients with oral cavity cancer?

Objective: To explore the utility of MR texture analysis (MRTA) for detection of nodal extracapsular spread (ECS) in oral cavity squamous cell carcinoma (SCC). Methods: 115 patients with oral cavity SCC treated with surgery and adjuvant (chemo)radiotherapy were identified retrospectively. First-order texture parameters (entropy, skewness and kurtosis) were extracted from tumour and nodal regions of interest (ROIs) using proprietary software (TexRAD). Nodal MR features associated with ECS (flare sign, irregular capsular contour; local infiltration; nodal necrosis) were reviewed and agreed in consensus by two experienced radiologists. Diagnostic performance characteristics of MR features of ECS were compared with primary tumour and nodal MRTA prediction using histology as the gold standard. Receiver operating characteristic (ROC) and regression analyses were also performed. Results: Nodal entropy derived from contrast-enhanced T1-weighted images was significant in predicting ECS (p = 0.018). MR features had varying accuracy: flare sign (70%); irregular contour (71%); local infiltration (66%); and nodal necrosis (64%). Nodal entropy combined with irregular contour was the best predictor of ECS (p = 0.004, accuracy 79%). Conclusion: First-order nodal MRTA combined with imaging features may improve ECS prediction in oral cavity SCC

Prediction of Patient Outcomes in Locally Advanced Cervical Carcinoma Following Chemoradiotherapy—Comparative Effectiveness of Magnetic Resonance Imaging and 2-Deoxy-2-[18F]fluoro-D-glucose Imaging

Purpose: To evaluate the utility and comparative effectiveness of three five-point qualitative scoring systems for assessing response on PET-CT and MRI imaging individually and in combination, following curative-intent chemoradiotherapy (CRT) in locally advanced cervical cancer (LACC). Their performance in the prediction of subsequent patient outcomes was also assessed; Methods: Ninety-seven patients with histologically confirmed LACC treated with CRT using standard institutional protocols at a single centre who underwent PET-CT and MRI at staging and post treatment were identified retrospectively from an institutional database. The post-CRT imaging studies were independently reviewed, and response assessed using five-point scoring tools for T2WI, DWI, and FDG PET-CT. Patient characteristics, staging, treatment, and follow-up details including progression-free survival (PFS) and overall survival (OS) outcomes were collected. To compare diagnostic performance metrics, a two-proportion z-test was employed. A Kaplan–Meier analysis (Mantel–Cox log-rank) was performed. Results: The T2WI (p < 0.00001, p < 0.00001) and DWI response scores (p < 0.00001, p = 0.0002) had higher specificity and accuracy than the PET-CT. The T2WI score had the highest positive predictive value (PPV), while the negative predictive value (NPV) was consistent across modalities. The combined MR scores maintained high NPV, PPV, specificity, and sensitivity, and the PET/MR consensus scores showed superior diagnostic accuracy and specificity compared to the PET-CT score alone (p = 0.02926, p = 0.0083). The Kaplan–Meier analysis revealed significant differences in the PFS based on the T2WI (p < 0.001), DWI (p < 0.001), combined MR (p = 0.003), and PET-CT/MR consensus scores (p < 0.001) and in the OS for the T2WI (p < 0.001), DWI (p < 0.001), and combined MR scores (p = 0.031) between responders and non-responders. Conclusion: Post-CRT response assessment using qualitative MR scoring and/or consensus PET-CT and MRI scoring was a better predictor of outcome compared to PET-CT assessment alone. This requires validation in a larger prospective study but offers the potential to help stratify patient follow-up in the future

Automated extraction of body composition metrics from abdominal CT or MR imaging: A scoping review

Purpose To review methodological approaches for automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and skeletal muscle from abdominal cross-sectional imaging for body composition analysis. Method Four databases were searched for publications describing automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and/or skeletal muscle from abdominal CT or MR imaging between 2019 and 2023. Included reports were evaluated to assess how imaging modality, cohort size, vertebral level, model dimensionality, and use of a volume or single slice affected segmentation accuracy and/or clinical utility. Exclusion criteria included reports not in English language, manual or semi-automated segmentation methods, reports prior to 2019 or solely of paediatric patients, and those not describing the use of abdominal CT or MR. Results After exclusions, 172 reports were included in the review. CT imaging was utilised approximately four times as often as MRI, and segmentation accuracy did not significantly differ between the two modalities. Cohort size had no significant effect on segmentation accuracy. There was little evidence to refute the current practice of extracting body composition metrics from the third lumbar vertebral level. There was no clear benefit of using a 3D model to perform segmentation over a 2D approach. Conclusion Automated segmentation of intra-abdominal soft tissues for body composition analysis is an intense area of research activity. Segmentation accuracy is not affected by cross-sectional imaging modality. Extracting metrics from a single slice at the third lumbar vertebral level is a common approach, however, extracting metrics from a volumetric slab surrounding this level may increase the resilience of the technique, which is important for clinical translation. A paucity of publicly available datasets led to most reports using different data sources, preventing direct comparison of segmentation techniques. Future efforts should prioritise creating a standardised dataset to facilitate benchmarking of different algorithms and subsequent clinical adoption

Harmonisation of scanner-dependent contrast variations in magnetic resonance imaging for radiation oncology, using style-blind auto-encoders.

Background and purpose Magnetic Resonance Imaging (MRI) exhibits scanner dependent contrast, which limits generalisability of radiomics and machine-learning for radiation oncology. Current deep-learning harmonisation requires paired data, retraining for new scanners and often suffers from geometry-shift which alters anatomical information. The aim of this study was to investigate style-blind auto-encoders for MRI harmonisation to accommodate unpaired training data, avoid geometry-shift and harmonise data from previously unseen scanners. Materials and methods A style-blind auto-encoder, using adversarial classification on the latent-space, was designed for MRI harmonisation. The public CC359 T1-w MRI brain dataset includes six scanners (three manufacturers, two field strengths), of which five were used for training. MRI from all six (including one unseen) scanner were harmonised to common contrast. Harmonisation extent was quantified via Kolmogorov-Smirnov testing of residual scanner dependence of 3D radiomic features, and compared to WhiteStripe normalisation. Anatomical content preservation was measured through change in structural similarity index on contrast-cycling (δSSIM). Results The percentage of radiomics features showing statistically significant scanner-dependence was reduced from 41% (WhiteStripe) to 16% for white matter and from 39% to 27% for grey matter. δSSIM < 0.0025 on harmonisation and de-harmonisation indicated excellent anatomical content preservation. Conclusions Our method harmonised MRI contrast effectively, preserved critical anatomical details at high fidelity, trained on unpaired data and allowed zero-shot harmonisation. Robust and clinically translatable harmonisation of MRI will enable generalisable radiomic and deep-learning models for a range of applications, including radiation oncology treatment stratification, planning and response monitoring

Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma

Background: Approximately 30% of patients with diffuse large B-cell lymphoma (DLBCL) will have recurrence. The aim of this study was to develop a radiomic based model derived from baseline PET/CT to predict 2-year event free survival (2-EFS). Methods: Patients with DLBCL treated with R-CHOP chemotherapy undergoing pre-treatment PET/CT between January 2008 and January 2018 were included. The dataset was split into training and internal unseen test sets (ratio 80:20). A logistic regression model using metabolic tumour volume (MTV) and six different machine learning classifiers created from clinical and radiomic features derived from the baseline PET/CT were trained and tuned using four-fold cross validation. The model with the highest mean validation receiver operator characteristic (ROC) curve area under the curve (AUC) was tested on the unseen test set. Results: 229 DLBCL patients met the inclusion criteria with 62 (27%) having 2-EFS events. The training cohort had 183 patients with 46 patients in the unseen test cohort. The model with the highest mean validation AUC combined clinical and radiomic features in a ridge regression model with a mean validation AUC of 0.75 ± 0.06 and a test AUC of 0.73. Conclusions: Radiomics based models demonstrate promise in predicting outcomes in DLBCL patients

Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation

2024 Frood, Willaime, Miles, Chambers, Al-Chalabi, Ali, Hougham, Brooks, Petrides, Naylor, Ward, Sulkin, Chaytor, Strouhal, Patel and Scarsbrook.Background: Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymphoma, with the time to evaluate such studies varying depending on the complexity of the case. Integrating artificial intelligence (AI) within the reporting workflow has the potential to improve quality and efficiency. The aims of the present study were to evaluate the influence of an integrated research prototype segmentation tool implemented within diagnostic PET/CT reading software on the speed and quality of reporting with variable levels of experience, and to assess the effect of the AI-assisted workflow on reader confidence and whether this tool influenced reporting behaviour. Methods: Nine blinded reporters (three trainees, three junior consultants and three senior consultants) from three UK centres participated in a two-part reader study. A total of 15 lymphoma staging PET/CT scans were evaluated twice: first, using a standard PET/CT reporting workflow; then, after a 6-week gap, with AI assistance incorporating pre-segmentation of disease sites within the reading software. An even split of PET/CT segmentations with gold standard (GS), false-positive (FP) over-contour or false-negative (FN) under-contour were provided. The read duration was calculated using file logs, while the report quality was independently assessed by two radiologists with &gt;15 years of experience. Confidence in AI assistance and identification of disease was assessed via online questionnaires for each case. Results: There was a significant decrease in time between non-AI and AI-assisted reads (median 15.0 vs. 13.3 min, p &lt; 0.001). Sub-analysis confirmed this was true for both junior (14.5 vs. 12.7 min, p = 0.03) and senior consultants (15.1 vs. 12.2 min, p = 0.03) but not for trainees (18.1 vs. 18.0 min, p = 0.2). There was no significant difference between report quality between reads. AI assistance provided a significant increase in confidence of disease identification (p &lt; 0.001). This held true when splitting the data into FN, GS and FP. In 19/88 cases, participants did not identify either FP (31.8%) or FN (11.4%) segmentations. This was significantly greater for trainees (13/30, 43.3%) than for junior (3/28, 10.7%, p = 0.05) and senior consultants (3/30, 10.0%, p = 0.05). Conclusions: The study findings indicate that an AI-assisted workflow achieves comparable performance to humans, demonstrating a marginal enhancement in reporting speed. Less experienced readers were more influenced by segmentation errors. An AI-assisted PET/CT reading workflow has the potential to increase reporting efficiency without adversely affecting quality, which could reduce costs and report turnaround times. These preliminary findings need to be confirmed in larger studies

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study

\ua9 Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. INTRODUCTION: In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. METHODS AND ANALYSIS: This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. ETHICS AND DISSEMINATION: This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. TRIAL REGISTRATION NUMBER: NCT05389774

Cell proliferation detected using [18F]FLT PET/CT as an early marker of abdominal aortic aneurysm

Background: Abdominal aortic aneurysm (AAA) is a focal aortic dilatation progressing towards rupture. Non-invasive AAA-associated cell proliferation biomarkers are not yet established. We investigated the feasibility of the cell proliferation radiotracer, fluorine-18-fluorothymidine ([18F]FLT) with positron emission tomography/computed tomography (PET/CT) in a progressive pre-clinical AAA model (angiotensin II, AngII infusion). Methods and Results: Fourteen-week-old apolipoprotein E-knockout (ApoE−/−) mice received saline or AngII via osmotic mini-pumps for 14 (n = 7 and 5, respectively) or 28 (n = 3 and 4, respectively) days and underwent 90-minute dynamic [18F]FLT PET/CT. Organs were harvested from independent cohorts for gamma counting, ultrasound scanning, and western blotting. [18F]FLT uptake was significantly greater in 14- (n = 5) and 28-day (n = 3) AAA than in saline control aortae (n = 5) (P < 0.001), which reduced between days 14 and 28. Whole-organ gamma counting confirmed greater [18F]FLT uptake in 14-day AAA (n = 9) compared to saline-infused aortae (n = 4) (P < 0.05), correlating positively with aortic volume (r = 0.71, P < 0.01). Fourteen-day AAA tissue showed increased expression of thymidine kinase-1, equilibrative nucleoside transporter (ENT)-1, ENT-2, concentrative nucleoside transporter (CNT)-1, and CNT-3 than 28-day AAA and saline control tissues (n = 3 each) (all P < 0.001). Conclusions: [18F]FLT uptake is increased during the active growth phase of the AAA model compared to saline control mice and late-stage AAA

Second-look PET-CT following an initial incomplete PET-CT response to (chemo)radiotherapy for head and neck squamous cell carcinoma

OBJECTIVES: The limited positive predictive value of an incomplete response on PET-CT following (chemo)radiotherapy for head and neck squamous cell carcinoma (HNSCC) means that the optimal management strategy remains uncertain. The aim of the study is to assess the utility of a 'second-look' interval PET-CT. METHODS: Patients with HNSCC who were treated with (chemo)radiotherapy between 2008 and 2017 and underwent (i) baseline and (ii) response assessment PET-CT and (iii) second-look PET-CT following incomplete (positive or equivocal scan) response were included. Endpoints were conversion rate to complete response (CR) and test characteristics of the second-look PET-CT. RESULTS: Five hundred sixty-two patients with HNSCC underwent response assessment PET-CT at a median of 17 weeks post-radiotherapy. Following an incomplete response on PET-CT, 40 patients underwent a second-look PET-CT at a median of 13 weeks (range 6-25) from the first response PET-CT. Thirty-four out of 40 (85%) patients had oropharyngeal carcinoma. Twenty-four out of 40 (60%) second-look PET-CT scans converted to a complete locoregional response. The primary tumour conversion rate was 15/27 (56%) and the lymph node conversion rate was 14/19 (74%). The sensitivity, specificity, positive predictive value and negative predictive value (NPV) of the second-look PET-CT were 75%, 75%, 25% and 96% for the primary tumour and 100%, 92%, 40% and 100% for lymph nodes. There were no cases of progression following conversion to CR in the primary site or lymph nodes. CONCLUSIONS: The majority of patients who undergo a second-look PET-CT convert to a CR. The NPV of a second-look PET-CT is high, suggesting the potential to avoid surgical intervention. KEY POINTS: • PET-CT is a useful tool for response assessment following (chemo)radiotherapy for head and neck squamous cell carcinoma. • An incomplete response on PET-CT has a limited positive predictive value and optimal management is uncertain. • These data show that with a 'second-look' interval PET-CT, the majority of patients convert to a complete metabolic response. When there is doubt about clinical and radiological response, a 'second-look' PET-CT can be used to spare patients unnecessary surgical intervention