Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes.

Objective (1): To develop and validate a machine learning (ML) model using radiomic features (RFs) extracted from [18F]FDG PET-CT to predict abdominal aortic aneurysm (AAA) growth rate. Methods (2): This retrospective study included 98 internal and 55 external AAA patients undergoing [18F]FDG PET-CT. RFs were extracted from manual segmentations of AAAs using PyRadiomics. Recursive feature elimination (RFE) reduced features for model optimisation. A multi-layer perceptron (MLP) was developed for AAA growth prediction and compared against Random Forest (RF), XGBoost, and Support Vector Machine (SVM). Accuracy was evaluated via cross-validation, with uncertainty quantified using dropout (MLP), standard deviation (RF), and 95% prediction intervals (XGBoost). External validation used independent data from two centres. Ground truth growth rates were calculated from serial ultrasound (US) measurements or CT volumes. Results (3): From 93 initial RFs, 29 remained after RFE. The MLP model achieved an MAE ± SEM of 1.35 ± 3.2e−4 mm/year with the full feature set and 1.35 ± 2.5e−4 mm/year with RFE. External validation yielded 1.8 ± 8.9e−8 mm/year. RF, XGBoost, and SVM models produced comparable accuracies internally (1.4–1.5 mm/year) but showed higher errors during external validation (1.9–1.97 mm/year). The MLP model demonstrated reduced uncertainty with the full feature set across all datasets. Conclusions (4): An MLP model leveraging [18F]FDG PET-CT radiomics accurately predicted AAA growth rates and generalised well to external data. In the future, more sophisticated stratification could guide individualised patient care, facilitating risk-tailored management of AAAs.

Abstract IntroductionAnal cancer is rare, but its incidence is increasing. Chemoradiotherapy is the primary treatment modality. Outcomes used in anal cancer trials vary which hinders evidence synthesis. Using a systematic review, patient interviews and a 2-stage Delphi consensus survey, the first CORMAC project brought together patients and healthcare professionals from across the world to agree shared priorities and make sure that studies of chemoradiotherapy treatments for anal cancer report outcomes that are meaningful to patients and health care professionals. CORMAC-1 established an internationally ratified core outcome set (COS) of 19 outcomes across 4 domains. These 19 outcomes are an agreed minimum that all clinical trials in chemoradiotherapy anal cancer trials should report. CORMAC-2 is the next phase which seeks to reach international agreement on the definitions for the 11 core outcomes in the domains of disease activity and survival. Agreeing definitions for these core outcomes will facilitate utilisation of the core outcome set, increasing outcome standardisation across trials thereby increasing the quality of data available for clinical decision-making and ultimately enhancing patient care.MethodsThe original CORMAC systematic review will be updated, focusing on 2 of the 4 COS domains, disease activity and survival domains. An international steering committee composed of international anal cancer trial experts will be formed. The committee will review the updated search results to develop a 2-stage Delphi consensus survey. The survey will be publicised through conferences, email lists, domestic and international bodies and will target healthcare and allied healthcare professional involved in the design, running, recruitment and publication of anal cancer trials. Following the 2-stage survey, a stakeholder meeting composed of the steering committee and selection of survey participants will ratify the results and agree a final set of core outcome definitions.Ethics and disseminationCORMAC-2 results will be disseminated through journal and conference publications to inform clinical teams and patient support groups to raise awareness and implementation of the core outcome set. Results will feed into the DECREASE study and it is registered with the Core Outcome Measures in Effectiveness Trials (COMET) initiative (1,2). As per the University of Manchester ethic decision tool, no ethical approval is required. Further information is available at https://cormacstudy.wordpress.com.

Abstract Objectives The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions. Methods MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology. Results After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was − 16.7%. Conclusion Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation. Clinical relevance statement Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation. Key points • Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation. Graphical Abstract

This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.

Objectives To assess the effectiveness of fluorine-18 fluorodeoxyglucose (FDG) positron-emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) for response assessment post curative-intent chemoradiotherapy (CRT) in anal squamous cell carcinoma (ASCC). Methods Consecutive ASCC patients treated with curative-intent CRT at a single centre between January 2018 and April 2020 were retrospectively identified. Clinical meta-data including progression-free survival (PFS) and overall survival (OS) outcomes were collated. Three radiologists evaluated PET-CT and MRI using qualitative response assessment criteria and agreed in consensus. Two-proportion z test was used to compare diagnostic performance metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy). Kaplan-Meier analysis (Mantel-Cox log-rank) was performed. Results MRI (accuracy 76%, PPV 44.8%, NPV 95.7%) and PET-CT (accuracy 69.3%, PPV 36.7%, NPV 91.1%) performance metrics were similar; when combined, there were statistically significant improvements (accuracy 94.7%, PPV 78.9%, NPV 100%). Kaplan-Meier analysis demonstrated significant differences in PFS between responders and non-responders at PET-CT ( p = 0.007), MRI ( p = 0.005), and consensus evaluation ( p < 0.001). Cox regression analysis of PFS demonstrated a lower hazard ratio (HR) and narrower 95% confidence intervals for consensus findings (HR = 0.093, p < 0.001). Seventy-five patients, of which 52 (69.3%) were females, with median follow-up of 17.8 months (range 5–32.6) were included. Fifteen of the 75 (20%) had persistent anorectal and/or nodal disease after CRT. Three patients died, median time to death 6.2 months (range 5–18.3). Conclusion Combined PET-CT and MRI response assessment post-CRT better predicts subsequent outcome than either modality alone. This could have valuable clinical benefits by guiding personalised risk-adapted patient follow-up. Key Points • MRI and PET-CT performance metrics for assessing response following chemoradiotherapy (CRT) in patients with anal squamous cell carcinoma (ASCC) were similar. • Combined MRI and PET-CT treatment response assessment 3 months after CRT in patients with ASCC was demonstrated to be superior to either modality alone. • A combined MRI and PET-CT assessment 3 months after CRT in patients with ASCC has the potential to improve accuracy and guide optimal patient management with a greater ability to predict outcome than either modality alone

Free AccessLetter to the EditorLetter to the editor: is total psoas muscle area at L3 truly representative of its volume?Alexandros Nicolaou Flaris and Thanos KonstantinidisAlexandros Nicolaou FlarisDepartment of Surgery, Tulane University, School of Medicine, New Orleans, LA 70112, Louisiana, United StatesSearch for more papers by this author and Thanos KonstantinidisDepartment of electrical and electronic engineering, Signal processing, Imperial College London, London SW7 2BU, UKSearch for more papers by this authorPublished Online:11 Mar 2021https://doi.org/10.1259/bjr.20210137SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail AboutCT measurements of the psoas muscle volume and surface area have been used as surrogate markers for sarcopenia.1 It is not clear whether psoas surface area can predict its volume nor is it clear at which level is it the most predictive of its volume.The article “Volumetric versus single slice measurements of core abdominal muscle for sarcopenia” by Waduud and colleagues2 attempts to clarify this situation. The authors calculated the psoas volume in 110 CT scans. They also calculated its area at five different levels, L2, L3, L4, L5, and at the sacrum. Their article concluded that “measurement of TPMA [total psoas muscle area] at L3 … is most representative of the TPMV [total psoas muscle volume]”. However, there are convincing reasons why that conclusion might not be correct.First, the authors did not measure TPMV. Rather, they used a “real-life” approximation because “not all clinicians… have access to specialist software for… [muscle volume calculation]”. Therefore, their conclusion does not answer the question accurately. To resolve the issue, a valid gold-standard is necessary, even if it requires specialized software. If the only available data were, as in the article,2 axial slices, then the gold-standard would have been the most informative model, i.e. the one which used all available slices (=“true” TPMV). If it was impossible to use the gold-standard, then a model that has been validated by comparing it to the gold-standard should have been used.Rather than using all the slices, the authors created a model based on five slices:TPMV=distanceL1−L2∗areaL2+...+distanceL5−sacrum∗areasacrum(1)where distancei1−i2 is the distance between the levels i1−i2 and areai2 is the psoas surface area at level i2. The article does not declare that this is a validated model, which means there is no documented connection between equation (1) and the “true” TPMV and this jeopardizes the study’s validity. One can argue that equation (1) is “good enough”; however, that has never been formally demonstrated.The authors subsequently ran five linear regressions on their model using the formula:TPMV=ai∗areai+b+error(2)where ai is the linear regression coefficient, i is the level, b is the intercept and error is the difference between predicted TPMV and observed TPMV for areai.The authors concluded that area at L3 is the most representative because it had the strongest association with psoas volume. This was based on the fact that the regression coefficient was the highest. However, the linear regression coefficient is not a valid measure of association between TPMA and TPMV. It is simply the slope of the linear model. The value which demonstrated the strength of association between TPMA and TPMV in their article2 is Pearson’s r. For the data presented in Figure 4, the highest correlation and therefore closest association were between area at L5 and TPMV (r=0.778), not L3 (r=0.627).As far as “representative” goes, it should not be used to characterize either correlation or regression. When the authors of the article2 said “most representative” they meant that the linear model that used the area at L3 to estimate TPMV was the model with the best fit. They used the linear regression to justify their conclusion, but, the parameter on which they based this conclusion on was (again) the regression coefficient.Goodness-of-fit does not depend on the regression coefficient, but rather on how small the error is, which means how close the model represents the data. It can be accurately estimated by such measures as the adjusted R2. The model with the larger adjusted R2 is the model with the better fit. As shown in Figure 4, the highest adjusted R2 was for the area at L5 (60.2%) not for L3 (38.8%).In summary, the authors used a non-validated linear model to calculate TPMV on which they ran a linear regression and did not base their inferences on the commonly accepted measures of goodness-of-fit. Therefore, the question as to whether the psoas area can predict its volume and if so, at which level, remains unanswered.REFERENCES1. Jones K, Gordon-Weeks A, Coleman C, Silva M. Radiologically determined sarcopenia predicts morbidity and mortality following abdominal surgery: a systematic review and meta-analysis. World J Surg 2017; 41: 2266–79. doi: https://doi.org/10.1007/s00268-017-3999-2 http://www.ncbi.nlm.nih.gov/pubmed/28386715 Crossref Medline ISI, Google Scholar2. Waduud MA, Adusumilli P, Drozd M, Bailey MA, Cuthbert G, Hammond C, et al.. Volumetric versus single slice measurements of core abdominal muscle for sarcopenia. Br J Radiol 2019; 92: 20180434. doi: https://doi.org/10.1259/bjr.20180434 http://www.ncbi.nlm.nih.gov/pubmed/30912955 Link, Google ScholarResponse to letter: is the total psoas muscle area at L3 truly representative of its volume1,2Mohammed Abdul Waduud, Michael Drozd and Pratik Adusumilli1Leeds Vascular Institute, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Great George Street, Leeds, LS1 3EX, United Kingdom2Leeds Institute of Cardiovascular and Metabolic Medicine, The University of Leeds, Clarendon Way, Leeds, LS2 9JT, United Kingdom3Department of Radiology, Leeds General Infirmary, Leeds Teaching Hospitals NHS Trust, Leeds, LS1 3EX, UKWe would like to thank Flaris and colleague for their insightful letter with regards to our paper: “Volumetric versus single slice measurements of core abdominal muscle for sarcopenia”.1We agree that a ‘valid gold-standard’ is important for the measurement of the total psoas muscle volume (TPMV). Whilst an estimation may not answer the question as accurately as the gold-standard, we believe it still provides important insights. It is important to acknowledge that other studies have also used variations in TPMV measurement including the multiplication of total psoas muscle area (TPMA) by the vertical distance over the psoas slices.2 Our rationale for using an estimation of TPMV is clearly described in the paper with extensive discussion of the limitations of this approach. The intention of the paper was to provide the radiomic research community with an insight into a potential relationship between single slice imaging, the self-proclaimed gold-standard in morphometric sarcopaenia research, and volumetric imaging.Measuring every 3–5 mm slice from the imaging sequence manually would have been extremely time consuming for an incremental technical gain. Although with different methodology to our paper, the relationship between single slice images of the psoas muscle area and psoas volume has since been validated.3,4 The letter highlights the real-life challenges encountered by clinicians undertaking research in this rapidly evolving field of computational modelling and health research.5,6 Furthermore, patient care in publicly funded health-care systems is often restricted by the burden of rising health-care costs, even out with a global pandemic. Therefore, it may not be feasible to routinely use costly software to reconstruct images for risk stratification.7 Even cost permitted, adopting this more robust approach may also come with some additional data security challenges exporting data from secure confidential databases especially within the NHS.Despite our interpretations and conclusions from our paper, the importance of transparency in the representation of the statistical tests are highlighted by this letter. This allows readers, such as Flaris and colleague, to interpret the data and draw their own evidence-based conclusions. The statistical expertise of the authorship of this letter provides an interesting opinion into mathematical approaches, namely linear regression and Pearsons’ correlation, when exploring relationships between parameters as those performed our study.1 We agree that the adjusted R-squared should have been adopted to interpret how well the data fit the model being tested, in favour of the Paersons’ correlation. The authors of this letter describe the rationale extensively which we support. Furthermore, the expert interpretation of our results by Flaris and colleague highlights the importance of involving mathematicians/statisticians with expertise in statistical modelling in this growing field of computational radiomic clinical research.Finally, irrespective of whether researchers have utilised measurements of TPMA at the third or fifth lumbar vertebrae or favour the TPMV in the past, there is an urgent need for a validated consensus agreement of morphometric sarcopaenia so that a “gold-standard” may be utilised for future research.REFERENCES1. Waduud MA, Adusumilli P, Drozd M, Bailey MA, Cuthbert G, Hammond C, et al.. Volumetric versus single slice measurements of core abdominal muscle for sarcopenia. Br J Radiol 2019; 92: 20180434. doi: https://doi.org/10.1259/bjr.20180434 http://www.ncbi.nlm.nih.gov/pubmed/30912955 Link, Google Scholar2. Womer AL, Brady JT, Kalisz K, Patel ND, Paspulati RM, Reynolds HL, et al.. Do psoas muscle area and volume correlate with postoperative complications in patients undergoing rectal cancer resection? Am J Surg 2018; 215: 503–6. doi: https://doi.org/10.1016/j.amjsurg.2017.10.052 http://www.ncbi.nlm.nih.gov/pubmed/29277239 Crossref Medline ISI, Google Scholar3. Kang MK, Kim KO, Kim MC, Park JG, Jang BI. Sarcopenia is a new risk factor of nonalcoholic fatty liver disease in patients with inflammatory bowel disease. Dig Dis 2020; 38: 507–14. doi: https://doi.org/10.1159/000506938 http://www.ncbi.nlm.nih.gov/pubmed/32135539 Crossref Medline ISI, Google Scholar4. Kleczynski P, Tokarek T, Dziewierz A, Sorysz D, Bagienski M, Rzeszutko L, et al.. Usefulness of psoas muscle area and volume and frailty scoring to predict outcomes after transcatheter aortic valve implantation. Am J Cardiol 2018; 122: 135–40. doi: https://doi.org/10.1016/j.amjcard.2018.03.020 http://www.ncbi.nlm.nih.gov/pubmed/29703441 Crossref Medline ISI, Google Scholar5. Barnes M, Hanson C, Giraud-Carrier C. The case for computational health science. J Healthc Inform Res 2018; 2: 99–110. doi: https://doi.org/10.1007/s41666-018-0024-y http://www.ncbi.nlm.nih.gov/pubmed/29974076 Crossref Medline, Google Scholar6. Kondylakis H, Axenie C, Kiran Bastola D, Katehakis DG, Kouroubali A, Kurz D, et al.. Status and recommendations of technological and data-driven innovations in cancer care: focus group study. J Med Internet Res 2020; 22: e22034. doi: https://doi.org/10.2196/22034 http://www.ncbi.nlm.nih.gov/pubmed/33320099 Crossref, Google Scholar7. Mujika KM, Méndez JAJ, de Miguel AF. Advantages and disadvantages in image processing with free software in radiology. J Med Syst 2018; 42: 36. doi: https://doi.org/10.1007/s10916-017-0888-z http://www.ncbi.nlm.nih.gov/pubmed/29333590 Crossref Medline, Google Scholar Previous article Next article FiguresReferencesRelatedDetails Volume 95, Issue 1135July 2022 © 2022 The Authors. Published by the British Institute of Radiology History ReceivedJanuary 21,2021AcceptedFebruary 01,2021Published onlineMarch 11,2021 Metrics Download PDF

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Objective: The measurement of muscle area is routinely utilised in determining sarcopaenia in clinical research. However, this simple measure fails to factor in age-related morphometric changes in muscle quality such as myosteatosis. The aims of this study were to: firstly investigate the relationship between the masseter area (quantity) and density (quality), and secondly compare the prognostic clinical relevance of each parameter. Methods: Cross-sectional CT head scans were reviewed for patients undergoing carotid endarterectomy. The masseter was manually delineated and the total masseter area (TMA) and the total masseter density (TMD) calculated. Measurements of the TMA were standardised against the cranial circumference. Observer variability in measurements were assessed using Bland-Altman plots. The relationship between TMA and TMD were evaluated using Pearson's correlation and linear regression analyses. The prognostic value of TMA and TMD were assessed using receiver operator curves and cox-regression analyses. Results: In total, 149 patients who had undergone routine CT scans prior to a carotid endarterectomy were included in this study. No significant observer variations were observed in measuring the TMA, TMD and cranium circumference. There was a significant positive correlation between standardised TMA and TMD (Pearson's correlation 0.426, p < 0.001, adjusted R-squared 17.6%). The area under the curve for standardised TMA in predicting all-cause mortality at 30 days, 1 year and 4 years were higher when compared to TMD. Standardised TMA was only predictive of post-operative overall all-cause mortality (adjusted hazard ratio 0.38, 95% confidence interval 0.15-0.97, p = 0.043). Conclusion: We demonstrate a strong relationship between muscle size and density. However, the utilisation of muscle area is likely to be limited in routine clinical care. Advances in knowledge: Our study supports the utilisation of muscle area in clinical sarcopaenia research. We did not observe any additional prognostic advantage in quantifying muscle density.

OBJECTIVE:We investigated whether total psoas muscle area (TPMA) was representative of the total psoas muscle volume (TPMV). Secondly, we assessed whether there was a relationship between the two commonly used single slice measurements of sarcopenia, TPMA and total abdominal muscle area (TAMA). METHODS:Pre-operative CT imaging of 110 patients undergoing elective endovascular aneurysm repair were analyzed by two trained independent observers. TPMA was measured at individual vertebral levels between the second lumbar vertebrae and sacrum. TPMV was also estimated between the second lumbar vertebrae and sacrum. TAMA was measured at the third lumbar vertebrae (L3). Observer differences were assessed using Bland-Altman plots. Associations between the different measures were assessed using linear regression and Pearson's correlation. RESULTS:We found single slice measurements of the TPMA to be representative of the TPMV at individual levels between L2 to the sacrum. The strongest association was seen at L3 [adjusted regression coefficient 16.7, 95% confidence interval (12.1 to 21.4), p < 0.001]. There was no association between TPMA and TAMA [adjusted regression coefficient -0.7, 95% confidence interval (-4.1 to 2.8), p = 0.710]. CONCLUSION:We demonstrate that measurements of the TPMA between L2 to the sacrum are representative of the TPMV, with the greatest association at the third lumbar vertebrae. There was no association between the TPMA and TAMA. ADVANCES IN KNOWLEDGE:We demonstrate that a single slice measurement of TPMA at L3 is representative of the muscle volume, contrary to previous criticism. Future sarcopenia studies can continue to measure TPMA which is representative of the TPMV.

AIM:To analyse the additional clinical value of protocol-driven and selective use of multidetector single-photon-emission tomography/computed tomography (SPECT/CT) in oncology patients undergoing whole-body bone scintigraphy (BS) and to analyse reporter confidence in diagnosis with and without SPECT/CT. MATERIALS AND METHODS:During a 2-year period, 2,954 whole-body BS examinations were performed in oncology patients, with 444 (15%) undergoing additional protocol-driven SPECT/CT. Retrospective evaluation of planar BS and SPECT/CT images was performed by two experienced dual-trained nuclear medicine radiologists. The BS and SPECT/CT images were graded blindly using a five-point scale designed to evaluate the likelihood of a lesion being benign or malignant. Interpretation was applied on a per-patient basis. RESULTS:There was a 74.5% increase in definitive diagnostic classification and a 26.6% reduction in equivocal findings with SPECT/CT when compared to BS alone (p<0001). Of cases initially classified as "probably benign" on BS, 5.1% (10/193) were reclassified to "probably malignant" (1%) or "malignant" (4.1%) using the SPECT/CT data. The highest impact in reporter confidence was seen with SPECT/CT in the interpretation of lesions within the pelvis (34%), ribs (23%), lumbar spine (22%), and thoracic spine (21%). CONCLUSION:Protocol-driven, selective use of SPECT/CT imaging to augment planar BS reduces equivocal findings and improves reporter confidence whilst minimising the impact on patient and reporting workflows.

Purpose: This retrospective study aimed to analyse the clinical value of additional focused hybrid multi-detector single positron emission tomography/computed tomography (SPECT/CT) performed selectively in a protocol-driven manner in whole bone scintigraphy (WBS) in oncology patients, by looking into the reporter confidence in diagnosis with and without SPECT/CT.

Ultrasound has a high degree of diagnostic accuracy in the assessment of rotator cuff tendons. Increasingly, ultrasound is being used to measure other parameters of rotator cuff pathology, including the size of the subacromial space, or acromiohumeral distance (AHD). Although this measure has been found to be clinically reliable, no assessment of its validity has been carried out. This technical study reports on the development of a novel ultrasound phantom of the shoulder and its use in validation of ultrasound measurement of AHD. There was a close agreement between AHD measures using ultrasound and the true subacromial space of the phantom model, providing support for the construct validity of this measurement. The phantom model has good potential for further development as a training tool for shoulder ultrasound and guided injections.

The purpose of this project was to create a sonographic phantom model of the shoulder that was accurate in bone configuration. Its main purpose was for operator training to measure the acromiohumeral distance. A computerized 3‐dimensional model of the superior half of the humerus and scapula was rendered and 3‐dimensionally printed. The bone model was embedded in a gelatin compound and set in a shoulder‐shaped mold. The materials used had speeds of sound that were well matched to soft tissue and epiphyseal bone. The model was specifically effective in simulating the acromiohumeral distance because of its accurate bone geometry.