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The narrative review article is focused on the strengths and limitations of modern imaging methods in the preoperative differential diagnosis of uterine mesenchymal tumours. In order to tailor the surgical procedures, imaging methods, namely ultrasound and magnetic resonance imaging (MRI), should be taken into account as well as clinical symptoms, age, and fertility plans. On ultrasound scans, uterine sarcomas have the appearance of large, usually solitary tumours of non-homogenous structure with irregular cysts, ill-defined outline borders (interrupted capsule), absence of calcifications with acoustic shadowing, and moderate to rich internal vascularisation. Rapid growth between follow-ups or atypical growth in peri- or post-menopause is also a sign of malignancy. On MRI, uterine sarcomas are characterized by irregular borders, hyperintense areas on T1-weighted and T2- weighted images, and central non-enhancing necrotic areas. On diffusion-weighted imaging (DWI/MRI), sarcomas exhibit markedly restricted diffusion but there is a significant overlap with some variants of fibroids. Core-needle or hysteroscopic biopsy can be used preoperatively if suspicious features are detected on ultrasound or MRI scans, particularly before myomectomy if fertility preservation is required or when conservative management is considered in asymptomatic women. Other imaging methods, such as positron emission tomography fused with CT (PET-CT) or computed tomography (CT) have limited role to distinguish uterine sarcomas from myomas and are suitable only for staging purposes. The importance of tumour markers including lactate dehydrogenase in preoperative work-up have not been verified yet. Conclusion: Uterine sarcomas can be distinguished from much more common myomas based on a combination of malignant features on ultrasound or MR imaging. In these suspicious cases the type and extent of surgery should be adjusted, avoiding intraperitoneal morcellation, which could lead to iatrogenic tumour spread and worsening of the patient's prognosis.

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CASE: This case report describes a patient who presented with clinical and radiographic features of a soft tissue sarcoma of the shoulder. Despite having a painless and relatively large mass, a biopsy and resection revealed nodular fasciitis (NF). CONCLUSION: This is an unusual case of a painless 10 cm mass that histopathologically was diagnosed as NF in the upper extremity with proximity to the axillary nerve and posterior humeral circumflex vessels. The USP6 rearrangement was helpful in confirming the diagnosis. Careful clinical, radiographic, and pathologic correlation is necessary in diagnosing these relatively rare tumors. In cases where there are discordant findings, molecular markers can be very helpful.

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PURPOSE: To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS). METHODS: In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models. RESULTS: The margin was considered as an independent predictor (p < 0.05). A total of 4 MRI features were selected and used to develop the radiomics signature. By incorporating the margin and radiomics signature, the developed nomogram showed the best prediction performance in the training (AUCs, margin vs. radiomics signature vs. nomogram, 0.609 vs. 0.909 vs. 0.910) and validation (AUCs, margin vs. radiomics signature vs. nomogram, 0.666 vs. 0.841 vs. 0.894) cohorts. DCA indicated potential usefulness of the nomogram model. CONCLUSIONS: This feasibility study evaluated predictive values of multi-parametric MRI for the prediction of lung metastasis, and proposed a nomogram model to potentially facilitate the individualized treatment decision-making for STSs.

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Fibrous dysplasia (FD) is a skeletal disorder characterized by the replacement of normal bone by fibrous tissue. Malignant transformation of FD is extremely rare and has been reported in both monostotic and polyostotic forms of FD. The most frequently reported malignant transformation is osteosarcoma. Among malignant bone tumors, spindle cell sarcomas are uncommon and difficult to diagnose. This report presents the case of a 30-year-old woman with an unusual presentation of a malignant undifferentiated spindle cell neoplasm secondary to fibrous dysplasia. The clinical features, radiological findings and management are discussed.

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PURPOSE: Lattice radiation therapy (LRT) alternates regions of high and low doses inside the tumour. Whilst this technique reported positive results in tumour size reduction, optimal lattice parameters are still unknown. We introduce an automated LRT planning method personalised to tumour shape and designed to allow investigation of lattice geometry. METHODS: Patients with retroperitoneal sarcoma were considered for inclusion. Automation was performed with the Eclipse Scripting Application Interface (v16, Varian Medical Systems, Palo Alto). By iterating over vertex size (V) and centre-to-centre distance (D), vertices were segmented within the gross tumour volume (GTV) in an alternating square pattern. Iterations stopped when the number of inserted vertices was contained between a prespecified lower and upper bound. Forty sets of lattices were considered, produced by varying V and D in five lower/upper bound pairs. Best-scoring sets were determined with a score favouring the maximization of GTV dose uniformity and heterogeneity whilst minimizing the maximum dose to organs at risk. RESULTS: Fifty patients with tumour volumes between 150 cm3 and 10,000 cm3 were included. Best-scoring sets were characterised by a low number of vertices (<15). Based on the best-scoring set, the predicted parameters to use for new patients were V = 0.19 (GTV volume)1/3 and D = 2V, in centimetres. The number of vertices (N) to insert in the GTV can be estimated with N ≤ (24 × 3% GTV volume)/(4πV3). CONCLUSIONS: The automated LRT treatment planning personalised to tumour size allows investigation of lattice geometry over a large range of GTV volumes.

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ABSTRACT: Primary prostatic stromal sarcoma is extremely rare. Serum PSA is usually normal. Here, we report a case of primary prostatic stromal sarcoma in a 23-year-old man. 18 F-prostate-specific membrane antigen (PSMA) PET/CT showed prostate mass and multiple low-density lesions in the liver with high PSMA expression. However, after chemotherapy, the level of PSMA expression in the prostate mass decreased, and PSMA expression lesions in the liver disappeared.

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OBJECTIVE: To develop a diagnostic model for predicting occult uterine sarcoma in patients with presumed uterine fibroids. MATERIALS AND METHODS: We retrospectively reviewed 41631 patients with presumed uterine fibroids who presented for HIFU treatment in 13 hospitals between November 2008 and October 2023. Of these patients, 27 with occult uterine sarcoma and 54 with uterine fibroids were enrolled. Univariate analysis and multivariate logistics regression analysis were used to determine the independent risk factors for the diagnosis of occult uterine sarcoma. A prediction model was constructed based on the coefficients of the risk factors. RESULTS: The multivariate analysis revealed abnormal vaginal bleeding, ill-defined boundary of tumor, hyperintensity on T2WI, and central unenhanced areas as independent risk factors. A scoring system was created to assess for occult uterine sarcoma risk. The score for abnormal vaginal bleeding was 56. The score for ill-defined lesion boundary was 90. The scores for lesions with hypointensity, isointensity signal/heterogeneous signal intensity, and hyperintensity on T2WI were 0, 42, and 93, respectively. The scores for lesions without enhancement on the mass margin, uniform enhancement of tumor, and no enhancement in the center of tumor were 0, 20, and 100, respectively. Patients with a higher total score implied a higher likelihood of a diagnosis of occult uterine sarcoma than that of patients with a lower score. The established model showed good predictive efficacy. CONCLUSIONS: Our results demonstrated that the diagnostic prediction model can be used to evaluate the risk of uterine sarcoma in patients with presumed uterine fibroids.

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PURPOSE: Radiation induced changes in bone such as radiation osteitis are commonly identified on magnetic resonance imaging (MRI) in patients who receive radiotherapy for soft tissue sarcoma (STS) management. This study proposes a novel MRI scoring system to assess osseous lesions and predict potential for malignancy based on MRI score in STS patients who received radiotherapy. METHODS: The MRI score consisted of 3 parameters: morphology, signal intensity, and progression. Interobserver reliability between MRI scores were analyzed with Cohen's kappa coefficient. Receiver operating curve (ROC) analysis was performed to determine a predictive MRI score for malignancy. RESULTS: 156 MRI's from 30 STS patients who received radiotherapy were retrospectively reviewed. Two (6.7 %) patients developed regional osseous metastasis identified on MRI. The kappa coefficient of the scoring system was 0.785 demonstrating substantial interobserver agreement (p < 0.001). ROC analysis demonstrated that the optimal cut-off value for malignant lesion on MRI was 5.5 (area under the curve 0.998; p < 0.001). CONCLUSIONS: This novel MRI scoring system recommends lesions with a score of six and above to be biopsied to distinguish if malignancy is present. We believe this scoring system can be utilized by multidisciplinary care teams to guide clinical recommendations for patients with STS and MRI findings concerning for malignancy versus radiation induced changes.

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ABSTRACT: Fibroblast activation protein (FAP) is a new promising molecular target for theragnostic approach. FAP inhibitors (FAPIs) labeled with 177Lu could be potentially a therapeutic radiopharmaceutical. Here, we presented the experience of 4 cycles of 177Lu-FAPI in a 67-year-old man with an unresectable mediastinal sarcoma.

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The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are part of the medical plan. Brain tumors are commonly imaged with magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). In this paper, multimodal fused imaging with classification and segmentation for brain tumors was proposed using the deep learning method. The MRI and CT brain tumor images of the same slices (308 slices of meningioma and sarcoma) are combined using three different types of pixel-level fusion methods. The presence/absence of a tumor is classified using the proposed Tumnet technique, and the tumor area is found accordingly. In the other case, Tumnet is also applied for single-modal MRI/CT (561 image slices) for classification. The proposed Tumnet was modeled with 5 convolutional layers, 3 pooling layers with ReLU activation function, and 3 fully connected layers. The first-order statistical fusion metrics for an average method of MRI-CT images are obtained as SSIM tissue at 83%, SSIM bone at 84%, accuracy at 90%, sensitivity at 96%, and specificity at 95%, and the second-order statistical fusion metrics are obtained as the standard deviation of fused images at 79% and entropy at 0.99. The entropy value confirms the presence of additional features in the fused image. The proposed Tumnet yields a sensitivity of 96%, an accuracy of 98%, a specificity of 99%, normalized values of the mean of 0.75, a standard deviation of 0.4, a variance of 0.16, and an entropy of 0.90.

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PURPOSE: Systematic reviews on the grading of STS using MRI are lacking. This review analyses the role of different MRI features in inferring the histological grade of STS. MATERIALS AND METHODS: A systematic review was conducted and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) checklist. The electronic databases of PubMed/MEDLINE were systematically searched for literature addressing the correlation of MRI findings in soft tissue sarcoma with tumor grade. As keywords "MRI", "magnetic resonance imaging", "sarcoma", "grade", "grading", and "FNCLCC" have been selected. RESULTS: 14 studies have been included in this systematic review. Tumor size (p = 0.015 (51 patients) to p = 0.81 (36 patients)), tumor margin (p < 0.001 (95 patients) to 0.93 (36 patients)), necrosis (p = 0.004 (50 patients) to p = 0.65 (95 patients)), peritumoral edema (p = 0.002 (130 patients) to p = 0.337 (40 patients)), contrast enhancement (p < 0.01 (50 patients) to 0.019 (51 patients)) and polycyclic/multilobulated tumor configuration (p = 0.008 (71 patients)) were significantly associated with STS malignancy grade in most of the included studies. Heterogeneity in T2w images (p = 0.003 (130 patients) to 0.202 (40 patients)), signal intensity in T1w images/ hemorrhage (p = 0.02 (130 patients) to 0.5 (31 patients)), peritumoral contrast enhancement (p < 0.001 (95 patients) to 0.253 (51 patients)) and tumoral diffusion restriction (p = 0.01 (51 patients) to 0.53 (52 patients)) were regarded as significantly associated with FNCLCC grade in some of the studies which investigated these features. Most other MRI features were not significant. CONCLUSION: Several MRI features, such as tumor size, necrosis, peritumoral edema, peritumoral contrast enhancement, intratumoral contrast enhancement, and polycyclic/multilobulated tumor configuration may indicate the malignancy grade of STS. However, further studies are needed to gain consensus.

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BACKGROUND: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). METHODS: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. RESULTS: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. CONCLUSION: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning.

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BACKGROUND: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression. METHODS: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis. RESULTS: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated. DATA CONCLUSION: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

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Prostatic stromal tumors, encompassing prostatic sarcoma and stromal tumors of uncertain malignant potential (STUMP), represent an exceedingly rare category of prostatic diseases, with a prevalence of less than 1%. We present a rare case involving a man in his early 40s diagnosed with STUMP. Despite presenting with normal prostate-specific antigen (PSA) concentrations, the patient experienced persistent dysuria and gross hematuria for >7 months, leading to an initial misdiagnosis of benign prostatic hyperplasia. Persistent symptoms prompted further investigation, with magnetic resonance imaging (MRI) revealing a suspicious lesion on the left side of the prostate, initially thought to be malignant. Transrectal prostatic biopsy subsequently confirmed the presence of mucinous liposarcoma, with no medical history of diabetes, coronary heart disease, or hypertension. The treatment approach comprised robot-assisted laparoscopic radical prostatectomy, culminating in a postoperative pathological definitive diagnosis of STUMP. This case underscores the indispensable role of early MRI in the diagnostic process, highlighting the necessity of detailed pathological examination for a conclusive diagnosis. Our report aims to illuminate the diagnostic challenges and potential treatment pathways for STUMP, emphasizing its consideration in the differential diagnosis of prostatic tumors to advance clinical outcomes in this rare but important condition.

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MRI serves as a critical step in the workup, local staging, and treatment planning of extremity soft-tissue masses. For the radiologist to meaningfully contribute to the management of soft-tissue masses, they need to provide a detailed list of descriptors of the lesion outlined in an organized report. While it is occasionally possible to use MRI to provide a diagnosis for patients with a mass, it is more often used to help with determining the differential diagnosis and planning of biopsies, surgery, radiation treatment, and chemotherapy (when provided). Each descriptor on the list outlined in this article is specifically aimed to assist in one or more facets of the overall approach to soft-tissue masses. This applies to all masses, but in particular sarcomas. Those descriptors are useful to help narrow the differential diagnosis and ensure concordance with a pathologic diagnosis and its accompanying grade assignment of soft-tissue sarcomas. These include a lesion's borders and shape, signal characteristics, and contrast enhancement pattern; the presence of peritumoral edema and peritumoral enhancement; and the presence of lymph nodes. The items most helpful in assisting surgical planning include a lesion's anatomic location, site of origin, size, location relative to a landmark, relationship to adjacent structures, and vascularity including feeding and draining vessels. The authors provide some background information on soft-tissue sarcomas, including their diagnosis and treatment, for the general radiologist and as a refresher for radiologists who are more experienced in tumor imaging. ©RSNA, 2024 See the invited commentary by Murphey in this issue.

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