Second opinions in orthopedic oncology imaging: can fellowship training reduce clinically significant discrepancies?

Soft tissue tumor imaging in adults: European Society of Musculoskeletal Radiology-Guidelines 2023-overview, and primary local imaging: how and where?

OBJECTIVES

Early, accurate diagnosis is crucial for the prognosis of patients with soft tissue sarcomas. To this end, standardization of imaging algorithms, technical requirements, and reporting is therefore a prerequisite. Since the first European Society of Musculoskeletal Radiology (ESSR) consensus in 2015, technical achievements, further insights into specific entities, and the revised WHO-classification (2020) and AJCC staging system (2017) made an update necessary. The guidelines are intended to support radiologists in their decision-making and contribute to interdisciplinary tumor board discussions.

MATERIALS AND METHODS

A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements were scored online by level of agreement (0 to 10) during two iterative rounds. Either "group consensus," "group agreement," or "lack of agreement" was achieved.

RESULTS

Eight sections were defined that finally contained 145 statements with comments. Overall, group consensus was reached in 95.9%, and group agreement in 4.1%. This communication contains the first part consisting of the imaging algorithm for suspected soft tissue tumors, methods for local imaging, and the role of tumor centers.

CONCLUSION

Ultrasound represents the initial triage imaging modality for accessible and small tumors. MRI is the modality of choice for the characterization and local staging of most soft tissue tumors. CT is indicated in special situations. In suspicious or likely malignant tumors, a specialist tumor center should be contacted for referral or teleradiologic second opinion. This should be done before performing a biopsy, without exception.

CLINICAL RELEVANCE

The updated ESSR soft tissue tumor imaging guidelines aim to provide best practice expert consensus for standardized imaging, to support radiologists in their decision-making, and to improve examination comparability both in individual patients and in future studies on individualized strategies.

KEY POINTS

• Ultrasound remains the best initial triage imaging modality for accessible and small suspected soft tissue tumors. • MRI is the modality of choice for the characterization and local staging of soft tissue tumors in most cases; CT is indicated in special situations. Suspicious or likely malignant tumors should undergo biopsy. • In patients with large, indeterminate or suspicious tumors, a tumor reference center should be contacted for referral or teleradiologic second opinion; this must be done before a biopsy.

Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics

OBJECTIVES

Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI.

MATERIALS AND METHODS

The research included 170 patients (mean age, 58 years ±12 (standard deviation), 84 men) with MSK lesions, who underwent MRI scans from April 2021 to May 2023. We proposed a deep learning (DL) segmentation model MSAPN based on multi-scale attention and pixel-level reconstruction, and compared it with existing algorithms. Using MSAPN-segmented lesions to extract their radiomic features for the benign and malignant classification of tumors.

RESULTS

Compared to the most advanced segmentation algorithms, MSAPN demonstrates better performance. The Dice similarity coefficients (DSC) are 0.871 and 0.815 in the testing set and independent validation set, respectively. The radiomics model for classifying benign and malignant lesions achieves an accuracy of 0.890. Moreover, there is no statistically significant difference between the radiomics model based on manual segmentation and MSAPN segmentation.

CONCLUSION

This research contributes to the advancement of MSK tumor diagnosis through automated segmentation and predictive classification. The integration of DL algorithms and radiomics shows promising results, and the visualization analysis of feature maps enhances clinical interpretability.

Musculoskeletal Radiology Education: A National Survey by the Italian College of Musculoskeletal Radiology

BACKGROUND

Our aim was to understand how musculoskeletal training is structured in Italian residency programmes and the needs of young trainees.

METHODS

We sent out an online questionnaire (17 questions) to Italian Society of Radiology residents and board-certified radiologists aged up to 39 years.

RESULTS

A total of 1144 out of 4210 (27.2%) members participated in the survey; 64.7% were residents and 35.3% were board-certified radiologists. Just 26.6% of participants had dedicated rotations for musculoskeletal training during their residency, although this percentage substantially increased in replies from northern Italy. One-fourth of residents had a scheduled period of musculoskeletal ultrasound. Most participants (76.3%) had <20 h per year of musculoskeletal lessons. The majority considered their musculoskeletal education poor (57.7%) or average (21.9%). According to 84.8% of replies, no dedicated training period about interventional musculoskeletal procedures was scheduled. Further, just 12.8% of residents took active part in such interventions. Nearly all participants believed that the musculoskeletal programme during residency needs to be improved, particularly concerning practices in ultrasound (92.8%), MRI cases interpretation/reporting (78.9%), and practice in ultrasound-guided interventional procedures (64.3%).

CONCLUSIONS

Despite some differences in the structure of musculoskeletal education provided by different regions, there is a shared demand for improvement in musculoskeletal training.

Automated Bone Tumor Segmentation and Classification as Benign or Malignant Using Computed Tomographic Imaging

The purpose of this study was to pair computed tomography (CT) imaging and machine learning for automated bone tumor segmentation and classification to aid clinicians in determining the need for biopsy. In this retrospective study (March 2005-October 2020), a dataset of 84 femur CT scans (50 females and 34 males, 20 years and older) with definitive histologic confirmation of bone lesion (71% malignant) were leveraged to perform automated tumor segmentation and classification. Our method involves a deep learning architecture that receives a DICOM slice and predicts (i) a segmentation mask over the estimated tumor region, and (ii) a corresponding class as benign or malignant. Class prediction for each case is then determined via majority voting. Statistical analysis was conducted via fivefold cross validation, with results reported as averages along with 95% confidence intervals. Despite the imbalance between benign and malignant cases in our dataset, our approach attains similar classification performances in specificity (75%) and sensitivity (79%). Average segmentation performance attains 56% Dice score and reaches up to 80% for an image slice in each scan. The proposed approach establishes the first steps in developing an automated deep learning method on bone tumor segmentation and classification from CT imaging. Our approach attains comparable quantitative performance to existing deep learning models using other imaging modalities, including X-ray. Moreover, visual analysis of bone tumor segmentation indicates that our model is capable of learning typical tumor characteristics and provides a promising direction in aiding the clinical decision process for biopsy.

Diagnostic errors in musculoskeletal oncology and possible mitigation strategies

The objective of this paper is to explore sources of diagnostic error in musculoskeletal oncology and potential strategies for mitigating them using case examples. As musculoskeletal tumors are often obvious, the diagnostic errors in musculoskeletal oncology are frequently cognitive. In our experience, the most encountered cognitive biases in musculoskeletal oncologic imaging are as follows: (1) anchoring bias, (2) premature closure, (3) hindsight bias, (4) availability bias, and (5) alliterative bias. Anchoring bias results from failing to adjust an early impression despite receiving additional contrary information. Premature closure is the cognitive equivalent of "satisfaction of search." Hindsight bias occurs when we retrospectively overestimate the likelihood of correctly interpreting the examination prospectively. In availability bias, the radiologist judges the probability of a diagnosis based on which diagnosis is most easily recalled. Finally, alliterative bias occurs when a prior radiologist's impression overly influences the diagnostic thinking of another radiologist on a subsequent exam. In addition to cognitive biases, it is also important for radiologists to acknowledge their feelings when making a diagnosis to recognize positive and negative impact of affect on decision making. While errors decrease with radiologist experience, the lack of application of medical knowledge is often the primary source of error rather than a deficiency of knowledge, emphasizing the need to foster clinical reasoning skills and assist cognition. Possible solutions for reducing error exist at both the individual and the system level and include (1) improvement in knowledge and experience, (2) improvement in clinical reasoning and decision-making skills, and (3) improvement in assisting cognition.

What does the orthopaedic surgeon want in the radiology report?

Complementary imaging is crucial in the diagnosis and management of the spectrum of Musculoskeletal (MSK) pathologies. Like in all medical specialities, its role in trauma and orthopaedic conditions has evolved. A radiology report following an imaging study should provide an accurate, timely interpretation of images and be presented in a format that allows formal analysis or clarification of a patient's diagnostic dilemma. It is essential that it is descriptive enough to allow clinico-pathological correlation to a patient's condition. A high-quality report follows clinical governance processes, provides clinical feedback, and when appropriate, incorporates advice regarding differential diagnosis or further investigation/management that can be undertaken, permitting the attending clinician to formulate a suitable treatment plan for their patient. In this narrative we explore common radiological investigations and reporting information in trauma and orthopaedic conditions, which would be useful to the attending surgeon.

Current status of MSK radiology training: an international survey by the European Society of Musculoskeletal Radiology (ESSR) Young Club

Objectives

There is wide variation between Countries in the structures of residency programmes, need for subspecialisation, and health care system organisation. This survey was aimed at gathering information regarding current musculoskeletal (MSK) educational programmes offered both in European and non-European Countries.

Methods

We administered an online survey to European Society of Radiology (ESR) residents and radiologists aged up to 35 years. The questionnaire was further disseminated by delegates of the ESR Radiology Trainees Forum. Survey consisted of 20 questions about the structure and organisation of MSK training programmes.

Results

Overall, 972 participants from 86 Countries completed the survey, with a wide heterogeneity of answers. Of them, 636 were residents (65.9%), 329 were certified radiologists (34.1%), with a mean age of 30.8 ± 3 years. Almost half of the participants had a dedicated MSK rotation/block during residency, with a duration of 3–6 months in 62.5% of cases. A dedicated period in MSK Ultrasound was present in only one-third of residency programmes; 38% of participants were expected to learn interventional MSK procedures, but only 28.2% have been actively involved in interventions during their residency. Overall, 62.7% of participants rated the quality of their MSK training as poor to average. Almost all (93.1%) thought that MSK training could be improved in their residency, especially ultrasound practice (80.7%) and MRI reporting (71.1%).

Conclusions

There are significant inconsistencies in the structure of MSK training offered by different Countries. Radiology trainees are showing substantial interest in MSK training, which necessitates strategic investments to standardise and enhance its quality.

Musculoskeletal radiology training in the UK: a national survey by the British Society of Skeletal Radiologists

AIM

To identify the standard of core and subspecialist musculoskeletal (MSK) training across deaneries in the UK.

MATERIALS AND METHODS

An online survey of 46 questions with responses in Likert scale or dichotomous formats was distributed to members of the Society of Radiologists in training, British Society of Skeletal Radiologists (BSSR), Training Programme Directors and the Royal College of Radiologists (RCR) Junior Radiology Forum representatives for national training schemes across the country. Responses were analysed descriptively with narrative analysis of free-text comments.

RESULTS

One hundred and seventy-eight participants completed the survey. Forty-six percent (81/178) were core trainees (ST1-3), 47% (84/178) were subspecialist trainees, and 7% (13/178) were newly qualified consultants (<2 years in post). All (178/178) of the participants had a dedicated MSK rotation, with a duration of ≥3 months in 76% (136/178). Only one-third received a dedicated period in MSK ultrasound and only 60% (107/178) had been actively involved in interventional procedures during their training. Overall, 21% (37/178) and 42% (75/178) of participants rated the quality of their MSK training as excellent and good, respectively. The majority (93%, 168/178) thought that MSK training could be improved, especially for ultrasound (62%, 110/178) and interventional computed tomography (CT) or fluoroscopy (57%, 101/178).

CONCLUSIONS

There are inconsistencies in MSK training offered in the UK. Although the majority of trainees are satisfied, there were gaps and potential threats to the quality of training. MSK training is witnessing substantial demand from trainees and workforce strategists necessitating tactical investments to standardise and enhance its quality.

Musculoskeletal Outside Interpretation (MOI-RADS): an automated quality assurance tool to prospectively track discrepancies in second-opinion interpretations in musculoskeletal imaging

OBJECTIVE

To implement an automated quality assurance tool to prospectively track discrepancies in musculoskeletal (MSK) exams submitted for second-opinion radiology interpretation at a tertiary center.

METHODS

From 2013 to 2020, a standardized template was included in re-interpretation MSK reports, and a concordance assessment compared with primary interpretation was assigned. Analysis of standardized template implementation and discordance rates was performed. Of the re-interpretations that demonstrated likely clinically relevant discordance, a sample was randomly selected and the EMR was reviewed to evaluate the impact on patient care and change in medical management.

RESULTS

A total of 1052 re-interpretations were identified using the standardized template. Services with higher requests for second-opinion interpretation were oncology (n = 351, 33%) and orthopedic surgery (n = 255, 24%). Overall utilization rate of the template was 65% with marked decreased during the last year (22% rate). In comparison to the primary report, there was a 30% discordance rate (n = 309) with 18% (n = 184) classified as likely clinically relevant. From the subset of discrepancies that could be clinically relevant, there was a change in management in 63% of the cases (19/30) with the re-interpretation ultimately proving correct in 80% of the cases (24/30).

CONCLUSION

Implementation of a quality assurance tool embedded in the radiology workflow of second-opinion interpretations can facilitate the analysis of patient care impact; however, stricter implementation is necessary. Oncologic studies were the most common indication for re-interpretations. Although the primary and second interpretations in the majority of cases were in agreement, subspecialty MSK radiology interpretation was shown to be more accurate than primary interpretations and impacted clinical management in cases of discrepancy.

Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient’s outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease.

Ten Commandments for the Diagnosis of Bone Tumors

The diagnosis of tumors and tumorlike lesions of bone is a routine part of both general and specialist radiologic practices. The spectrum of disorders ranges from the small incidental lesion to the potentially life-limiting malignancies whether primary or secondary. In this review, authored by experts from several European orthopaedic oncology centers, we present a collection of pieces of advice in the form of 10 commandments. Adherence in daily practice to this guidance should help minimize adverse patient experiences and outcomes.

Integration of Peer Review in PACS Results in a Marked Increase in the Discrepancies Reported

OBJECTIVE. The objective of this article is to assess the impact of integrating peer review in PACS on the reporting of discrepancies. Our hypothesis is that a PACS-integrated machine-randomized and semiblinded peer review tool leads to an increase in discrepancies reported. MATERIALS AND METHODS. A PACS tool was implemented to prompt radiologists to perform peer review of prior comparison studies in a randomized fashion. The reviewed radiologist's name was omitted from the prior report in PACS. Before this implementation, radiologists entered peer reviews directly on the RADPEER website. Three academic subspecialty sections comprising 24 radiologists adopted the tool (adopters group). Three sections comprising 14 radiologists did not adopt the tool (nonadopters group). Peer review submissions were analyzed for 4 months before and 4 months after the implementation. The mean rate of significant discrepancies (RADPEER score 2b or higher) reported per radiologist was calculated and the discrepancy rates of the periods before and after the implementation were compared. RESULTS. The mean significant discrepancy rate reported per radiologist in the adopters group increased from 0.19% ± 0.46% (SD) before the implementation to 0.93% ± 1.45% after implementation (p = 0.01). No significant discrepancies were reported by the nonadopters group in either period. CONCLUSION. In this single institutional retrospective analysis, integrating peer review in PACS resulted in a fivefold increase in reported significant discrepancies. These results suggest that peer review data are influenced by the design of the tool used including PACS integration, randomization, and blinding.