Development of a risk assessment model to differentiate malignant and benign musculoskeletal soft‐tissue masses on magnetic resonance imaging

Obaid, Haron; Vassos, Nicholas; Adams, Scott; Bryce, Rhonda; Donuru, Achala; Sinclair, Nicolette

doi:10.1111/1754-9485.12981

Cited by 8 publications

(19 citation statements)

References 21 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve differentiation between benign and malignant STMs on Magnetic resonance (MR) imaging, Obaid et al created a risk stratification model including criteria, such as edema, intralesional fat and signal intensity patterns as well as lesion size in a series of 110 patients [11]. Although average malignant lesions showed a larger diameter, approximately 15% of lesions with a diameter of less than 5 cm were malignant in their study.…”

Section: Introductionmentioning

confidence: 99%

Soft tissue masses: distribution of entities and rate of malignancy in small lesions

Gassert

Specht

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

Background Small soft tissue masses are often falsely assumed to be benign and resected with failure to achieve tumor-free margins. Therefore, this study retrospectively investigated the distribution of histopathologic diagnosis to be encountered in small soft tissue tumors (≤ 5 cm) in a large series of a tertiary referral center. Methods Patients with a soft tissue mass (STM) with a maximum diameter of 5 cm presenting at our institution over a period of 10 years, who had undergone preoperative Magnetic resonance imaging and consequent biopsy or/and surgical resection, were included in this study. A final histopathological diagnosis was available in all cases. The maximum tumor diameter was determined on MR images by one radiologist. Moreover, tumor localization (head/neck, trunk, upper extremity, lower extremity, hand, foot) and depth (superficial / deep to fascia) were assessed. Results In total, histopathologic results and MR images of 1753 patients were reviewed. Eight hundred seventy patients (49.63%) showed a STM ≤ 5 cm and were therefore included in this study (46.79 +/− 18.08 years, 464 women). Mean maximum diameter of the assessed STMs was 2.88 cm. Of 870 analyzed lesions ≤ 5 cm, 170 (19.54%) were classified as superficial and 700 (80.46%) as deep. The malignancy rate of all lesions ≤ 5 cm was at 22.41% (superficial: 23.53% / deep: 22.14%). The malignancy rate dropped to 16.49% (20.79% / 15.32%) when assessing lesions ≤ 3 cm (p = 0.007) and to 15.0% (18.18% / 13.79%) when assessing lesions ≤ 2 cm (p = 0.006). Overall, lipoma was the most common benign lesion of superficial STMs (29.41%) and tenosynovial giant cell tumor was the most common benign lesion of deep STMs (23.29%). Undifferentiated pleomorphic sarcoma was the most common malignant diagnosis among both, superficial (5.29%) and deep (3.57%) STMs. Conclusions The rate of malignancy decreased significantly with tumor size in both, superficial and deep STMs. The distribution of entities was different between superficial and deep STMs, yet there was no significant difference found in the malignancy rate.

show abstract

Section: Introductionmentioning

confidence: 99%

Soft tissue masses: distribution of entities and rate of malignancy in small lesions

Gassert

Specht

et al. 2021

BMC Cancer

View full text Add to dashboard Cite

show abstract

“…However, there are more than 200 diverse subtypes of soft tissue and bone tumors. Moreover, many lesions exhibit nonspecific morphological appearances; therefore, the discrimination between malignant and benign tumors using conventional imaging modalities is challenging and often leads to misinterpretation [ 4 , 5 ]. Accurate discrimination between malignant and benign soft tissue and bone tumors is essential for the prevention of unnecessary pathological biopsies and unplanned surgical resections.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F-FDG PET/CT-derived parameters

et al. 2020

View full text Add to dashboard Cite

Background Accurate differentiation between malignant and benign changes in soft tissue and bone lesions is essential for the prevention of unnecessary biopsies and surgical resection. Nevertheless, it remains a challenge and a standard diagnosis modality is urgently needed. The objective of this study was to evaluate the usefulness of 18 F-fluorodeoxyglucose ( 18 F-FDG) PET/CT-derived parameters to differentiate soft tissue sarcoma (STS) and bone sarcoma (BS) from benign lesions. Methods Patients who had undergone pre-treatment 18 F-FDG PET/CT imaging and subsequent pathological diagnoses to confirm malignant (STS and BS, n = 37) and benign ( n = 33) soft tissue and bone lesions were retrospectively reviewed. The tumor size, PET and low-dose CT visual characteristics, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneous factor (HF) of each lesion were measured. Univariate and multivariate logistic regression analyses were conducted to determine the significant risk factors to distinguish sarcoma from benign lesions. To establish a regression model based on independent risk factors, and the receiver operating characteristic curves (ROCs) of individual parameters and their combination were plotted and compared. Conventional imaging scans were re-analyzed, and the diagnostic performance compared with the regression model. Results Univariate analysis results revealed that tumor size, SUVmax, MTV, TLG, and HF of 18 F-FDG PET/CT imaging in the STS and BS group were all higher than in the benign lesions group (all P values were < 0.01). The differences in the visual characteristics between the two groups were also all statistically significant ( P < 0.05). However, the multivariate regression model only included SUVmax and HF as independent risk factors, for which the odds ratios were 1.135 (95%CI: 1.026 ~ 1.256, P = 0.014) and 7.869 (95%CI: 2.119 ~ 29.230, P = 0.002), respectively. The regression model was constructed using the following expression: Logit ( P ) = − 2.461 + 0.127SUVmax + 2.063HF. The area under the ROC was 0.860, which was higher than SUVmax (0.744) and HF (0.790). The diagnostic performance of the regression model was superior to those of individual parameters and conventional imaging. Conclusion The regression model including SUVmax and HF based on 18 F-FDG PET/CT imaging may be useful for differentiating STS and BS from benign lesions.

show abstract

“…However, there are more than 200 diverse subtypes of soft tissue and bone tumors. Moreover, many lesions exhibit nonspeci c morphological appearances; therefore, the discrimination between malignant and benign tumors using conventional imaging modalities is challenging and often leads to misinterpretation [4,5]. Accurate discrimination between malignant and benign soft tissue and bone tumors is essential for the prevention of unnecessary pathological biopsies and unplanned surgical resections.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F- FDG PET/CT-derived parameters

Chen

Feng

Xie

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Accurate differentiation between malignant and benign changes in soft tissue and bone lesions is essential for the prevention of unnecessary biopsies and surgical resection. Nevertheless, it remains a challenge and a standard diagnosis modality is urgently needed. The objective of this study was to evaluate the usefulness of 18F-fluorodeoxyglucose (18F-FDG) PET/CT-derived parameters to differentiate soft tissue sarcoma (STS) and bone sarcoma (BS) from benign lesions. Methods: Patients who had undergone pre-treatment 18F-FDG PET/CT imaging and subsequent pathological diagnoses to confirm malignant (STS and BS, n = 37) and benign (n = 33) soft tissue and bone lesions were retrospectively reviewed. The tumor size, PET and low-dose CT visual characteristics, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneous factor (HF) of each lesion were measured. Univariate and multivariate logistic regression analyses were conducted to determine the significant risk factors to distinguish sarcoma from benign lesions. To establish a regression model based on independent risk factors, and the receiver operating characteristic curves (ROCs)of individual parameters and their combination were plotted and compared. Conventional imaging scans were re-analyzed, and the diagnostic performance compared with the regression model.Results: Univariate analysis results revealed that tumor size, SUVmax, MTV, TLG, and HF of 18F-FDG PET/CT imaging in the STS and BS group were all higher than in the benign lesions group (all P values were <0.01). The differences in the visual characteristics between the two groups were also all statistically significant (P <0.05). However, the multivariate regression model only included SUVmax and HF as independent risk factors, for which the odds ratios were 1.135 (95%CI: 1.026~1.256, P = 0.014) and 7.869 (95%CI: 2.119~29.230, P = 0.002), respectively. The regression model was constructed using the following expression: Logit (P) = -2.461+0.127SUVmax+2.063HF. The area under the ROC was 0.860, which was higher than SUVmax (0.744) and HF (0.790). The diagnostic performance of the regression model was superior to those of individual parameters and conventional imaging.Conclusion: The regression model including SUVmax and HF based on 18F-FDG PET/CT imaging may be useful for differentiating STS and BS from benign lesions.

show abstract

Development of a risk assessment model to differentiate malignant and benign musculoskeletal soft‐tissue masses on magnetic resonance imaging

Cited by 8 publications

References 21 publications

Soft tissue masses: distribution of entities and rate of malignancy in small lesions

Soft tissue masses: distribution of entities and rate of malignancy in small lesions

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F-FDG PET/CT-derived parameters

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F- FDG PET/CT-derived parameters

Contact Info

Product

Resources

About