Open Source Journal Articles

Prevalence of MRI-Detected Ankle Injuries in Athletes in the Rio de Janeiro 2016 Summer Olympics
Authors: Rafael Heiss, MD, Ali Guermazi, MD, PhD, Mohamed Jarraya, MD, Lars Engebretsen, MD, PhD, Thilo Hotfiel, MD, Pedram Parva, MD, Frank W. Roemer, MD

Objective
To describe the prevalence, severity, and location of ankle injuries as assessed on magnetic resonance imaging (MRI) in athletes participating in the Rio de Janeiro 2016 Summer Olympic Games.

Methods
We analyzed all ankle MRIs that were acquired for suspected injury as reported by the National Olympic Committee medical teams and the Organizing Committee medical staff during the Rio 2016 Summer Olympics. Diagnostic imaging was performed through the Olympic Village Polyclinic. Images were interpreted retrospectively according to standardized criteria.

Results
A total of 11,274 athletes participated in the Games, of which 89 (8.8%) were referred for an ankle MRI. Eighty-eight of the 89 (99%) had at least 1 abnormal finding, and some had as many as 27, for an average of 6.2 abnormalities per examination. Around one-fifth of all abnormal findings were considered pre-existing (21%) and 79% were assumed to be the result of an acute or subacute injury. The highest proportion of acute/subacute injuries per athlete occurred in ball sports (7.0 injuries per examination) and in the age group >30. Most pre-existing findings per athlete were identified in the group of others (no track and field or ball sports athletes) with 2.5 findings per examination and respectively in the age group >30 (1.7).

Conclusion
Our study demonstrated a high prevalence of acute and subacute, but also pre-existing injuries in Olympic athletes undergoing ankle MRI. Tendon injuries were the most common acute injuries, found mainly in ball sports athletes. Most pre-existing ankle injuries were identified at the ligaments.

Lung cancer detected on coronary artery calcium scoring computed tomography: factors delaying diagnosis and predictors of survival
Authors: Jin Young Kim, Young Joo Suh, Kyungsun Nam, Byoung Wook Choi

Background
Lung cancers are occasionally detected on coronary artery calcium (CAC)-scoring computed tomography (CT). However, the cause of delayed diagnosis and prognostic factors have not been studied.

Purpose
To investigate the causes of delayed diagnosis of lung cancer in patients who undergo CAC-scoring CT and to identify predictors of mortality.

Material and Methods
A total of 151 patients who were diagnosed with lung cancer and had undergone CAC-scoring CT from January 2010 to December 2014 were retrospectively enrolled. The reasons for delayed diagnosis were reviewed. Follow-up data on all-cause mortality were obtained. Cox proportional hazards regression analysis was used to identify predictors of mortality. Analyses of solid and subsolid subgroups were performed.

Results
Among the 151 patients, 86 lesions (56.9%) were solid and 63 (41.7%) were subsolid. The main causes of delayed diagnosis were detection (48%) and interpretation (22%) errors. Age, size, unresectable stage at the time of diagnosis, and stage shift were independent prognostic factors throughout the entire and in the solid subgroup (all P < 0.2). There were no significant prognostic factors in the subsolid subgroup.

Conclusion
In conclusion, avoidance of detection and interpretation errors may prevent delayed diagnosis of lung cancer on CAC-scoring CT. Older age, larger tumor size, unresectable stage at the time of diagnosis, and stage shift were associated with poor survival in patients with solid lung cancers but not in those with subsolid lung cancers.

Evaluation of Lower-Dose Spiral Head CT for Detection of Intracranial Findings Causing Neurologic Deficits
Authors: J.G. Fletcher, D.R. DeLone, A.L. Kotsenas, N.G. Campeau, V.T. Lehman, L. Yu, S. Leng, D.R. Holmes, P.K. Edwards, M.P. Johnson, G.J. Michalak, R.E. Carter and C.H. McCollough

BACKGROUND AND PURPOSE: Despite the frequent use of unenhanced head CT for the detection of acute neurologic deficit, the radiation dose for this exam varies widely. Our aim was to evaluate the performance of lower-dose head CT for detection of intracranial findings resulting in acute neurologic deficit.

MATERIALS AND METHODS: Projection data from 83 patients undergoing unenhanced spiral head CT for suspected neurologic deficits were collected. Cases positive for infarction, intra-axial hemorrhage, mass, or extra-axial hemorrhage required confirmation by histopathology, surgery, progression of findings, or corresponding neurologic deficit; cases negative for these target diagnoses required negative assessments by two neuroradiologists and a clinical neurologist. A routine dose head CT was obtained using 250 effective mAs and iterative reconstruction. Lower-dose configurations were reconstructed (25-effective mAs iterative reconstruction, 50-effective mAs filtered back-projection and iterative reconstruction, 100-effective mAs filtered back-projection and iterative reconstruction, 200-effective mAs filtered back-projection). Three neuroradiologists circled findings, indicating diagnosis, confidence (0–100), and image quality. The difference between the jackknife alternative free-response receiver operating characteristic figure of merit at routine and lower-dose configurations was estimated. A lower 95% CI estimate of the difference greater than −0.10 indicated noninferiority.

RESULTS: Forty-two of 83 patients had 70 intracranial findings (29 infarcts, 25 masses, 10 extra- and 6 intra-axial hemorrhages) at routine head CT (CT dose index = 38.3 mGy). The routine-dose jackknife alternative free-response receiver operating characteristic figure of merit was 0.87 (95% CI, 0.81–0.93). Noninferiority was shown for 100-effective mAs iterative reconstruction (figure of merit difference, −0.04; 95% CI, −0.08 to 0.004) and 200-effective mAs filtered back-projection (−0.02; 95% CI, −0.06 to 0.02) but not for 100-effective mAs filtered back-projection (−0.06; 95% CI, −0.10 to −0.02) or lower-dose levels. Image quality was better at higher-dose levels and with iterative reconstruction (P < .05).

CONCLUSIONS: Observer performance for dose levels using 100–200 eff mAs was noninferior to that observed at 250 effective mAs with iterative reconstruction, with iterative reconstruction preserving noninferiority at a mean CT dose index of 15.2 mGy.

Paracoccidioidomycosis of the Central Nervous System: CT and MR Imaging Findings
Authors: M. Rosa Júnior, A.C. Amorim, I.V. Baldon, L.A. Martins, R.M. Pereira, R.P. Campos, S.S. Gonçalves, T.R.G. Velloso, P. Peçanha and A. Falqueto

BACKGROUND AND PURPOSE: Paracoccidioidomycosis is a fungal infection mainly caused by the thermodimorphic fungus Paracoccidioides. The purpose of our study was to demonstrate the neuroimaging findings from 24 patients with CNS paracoccidioidomycosis.

MATERIALS AND METHODS: We performed a retrospective analysis focusing on the radiologic characteristics of CNS paracoccidioidomycosis. The 24 selected patients underwent MR imaging and/or CT, and the diagnosis was made by the presence of typical neuroimaging features, combined with fungus isolation, a serologic test, or the presence of disseminated disease.

RESULTS: Headache was the most common neurologic symptom, while the pseudotumoral form was the most common pattern. The number of lesions ranged from 1 to 11, with most localized on the frontal lobe with >2-cm lesions. CT showed mainly hypoattenuating lesions, whereas MR imaging demonstrated mainly hyposignal lesions on T1WI and T2WI. Furthermore, ring enhancement was present in most patients. The “dual rim sign” on SWI occurred in 100% of our patients with lesions of >2 cm.

CONCLUSIONS: The diagnosis of CNS paracoccidioidomycosis is difficult. Nevertheless, imaging examinations can play an important role in the diagnosis and evaluation of the disease.

Recent developments in non-coplanar radiotherapy
Authors: Gregory Smyth, PhD, Philip M Evans, DPhil, Jeffrey C Bamber, PhD and James L Bedford, PhD

This paper gives an overview of recent developments in non-coplanar intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT). Modern linear accelerators are capable of automating motion around multiple axes, allowing efficient delivery of highly non-coplanar radiotherapy techniques. Novel techniques developed for C-arm and non-standard linac geometries, methods of optimization, and clinical applications are reviewed. The additional degrees of freedom are shown to increase the therapeutic ratio, either through dose escalation to the target or dose reduction to functionally important organs at risk, by multiple research groups. Although significant work is still needed to translate these new non-coplanar radiotherapy techniques into the clinic, clinical implementation should be prioritized. Recent developments in non-coplanar radiotherapy demonstrate that it continues to have a place in modern cancer treatment.

Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features
Authors: Usman Bashir, FRCR, MD, Bhavin Kawa, FRCR , Muhammad Siddique, PhD , Sze Mun Mak, FRCR, Arjun Nair, FRCR, MD, Emma Mclean, FRCPath, Andrea Bille, FRCS, PhD, Vicky Goh, FRCR, MD and Gary Cook, FRCR, MD

OBJECTIVE:
Non-invasive distinction between squamous cell carcinoma and adenocarcinoma subtypes of non-small-cell lung cancer (NSCLC) may be beneficial to patients unfit for invasive diagnostic procedures or when tissue is insufficient for diagnosis. The purpose of our study was to compare the performance of random forest algorithms utilizing CT radiomics and/or semantic features in classifying NSCLC.

METHODS:
Two thoracic radiologists scored 11 semantic features on CT scans of 106 patients with NSCLC. A set of 115 radiomics features was extracted from the CT scans. Random forest models were developed from semantic (RM-sem), radiomics (RM-rad), and all features combined (RM-all). External validation of models was performed using an independent test data set (n = 100) of CT scans. Model performance was measured with out-of-bag error and area under curve (AUC), and compared using receiver-operating characteristics curve analysis on the test data set.

RESULTS:
The median (interquartile-range) error rates of the models were: RF-sem 24.5 % (22.6 – 37.5 %), RF-rad 35.8 % (34.9 – 38.7 %), and RM-all 37.7 % (37.7 – 37.7). On training data, both RF-rad and RF-all gave perfect discrimination (AUC = 1), which was significantly higher than that achieved by RF-sem (AUC = 0.78; p < 0.0001). On test data, however, RM-sem model (AUC = 0.82) out-performed RM-rad and RM-all (AUC = 0.5 and AUC = 0.56; p < 0.0001), neither of which was significantly different from random guess ( p = 0.9 and 0.6 respectively).

CONCLUSION:
Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data.

ADVANCES IN KNOWLEDGE:
Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.

Detection of avascular necrosis on routine diffusion-weighted whole body MRI in patients with multiple myeloma
Authors: Naeem Ahmed, MBBS, BSc, FRCR, Priya Sriskandarajah, MBBS, BSc, MRCP, Christian Burd, FRCR, Angela Riddell, BSc, MBBS, FRCS, FRCR, MD, Kevin Boyd, MBBS, BSc, MRCP FRCPath PhD, Martin Kaiser, MD, RWTH 2 and Christina Messiou, MD, BMedSci, BMBS, MRCP, F

OBJECTIVE:
Current therapies for multiple myeloma, which include corticosteroids, increase risk of avascular necrosis. The aim of this study was to assess incidental detection of femoral head avascular necrosis on routine whole body MRI including diffusion weighted MRI.

METHODS:
All whole body MRI studies, performed on patients with known multiple myeloma between 1 January 2010 to 1 May 2017 were assessed for features of avascular necrosis.

RESULTS:
650 whole body MR scans were analysed. 15 patients (6.6%) had typical MR features of avascular necrosis: 2/15 (13.3%) had femoral head collapse, 4/15 (26.7%) had bilateral avascular necrosis and 9/15 (60%) were asymptomatic.

CONCLUSION:
This is the first report of avascular necrosis detected on routine whole body MRI in patients with multiple myeloma. Targeted review of femoral heads in multiple myeloma patients undergoing whole body MR is recommended, including in patients without symptoms.

ADVANCES IN KNOWLEDGE:
Whole body MR which includes diffusion-weighted MRI is extremely sensitive for evaluation of bone marrow. Although whole body MRI is primarily used for evaluation of multiple myeloma disease burden, it also presents an unique opportunity to evaluate the femoral heads for signs of avascular necrosis which can predate symptoms.

Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.

Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.