Can MRI differentiate between atypical cartilaginous tumors and high-grade chondrosarcoma? A systematic review.
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ABSTRACT:
SUBMITTER: Deckers C
PROVIDER: S-EPMC8023913 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAtypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones.MethodsOne-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test.FindingsAfter tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134).InterpretationMachine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features.FundingESSR Young Researchers Grant.
| S-EPMC8688587 | biostudies-literature
Project description:Fluoroscopy is currently the standard imaging modality for curettage of atypical cartilaginous tumors/chondrosarcoma grade 1 (ACT/CS1). Computer-assisted surgery (CAS) is a possible alternative, offering higher resolution imaging and continuous three-dimensional feedback without ionizing radiation use. CAS hypothetically makes curettage more accurate, thereby decreasing residue or recurrence rate. This study aims to compare CAS and fluoroscopy in curettage of ACT/CS1.A single center retrospective cohort study was performed. CAS and fluoroscopy were used in parallel. Included were patients who had curettage for ACT/CS1in the long bones, with a minimum follow-up of 24 months. Tumor volume was determined on pre-operative MRI scans. Outcome comprised local recurrence rates, residue rates, complications and procedure time.Seventy-seven patients were included, 17 in the CAS cohort, 60 in the fluoroscopy cohort. Tumor volume was significantly larger in the CAS cohort (p = 0.04). There were no recurrences in either group. Residual tumor (2/17 vs. 7/60), complications did not differ significantly: fracture rate (3/17 vs. 6/60); nor did surgical time (1.26h vs. 1.34h).CAS curettage showed good oncologic results. Outcome was comparable to fluoroscopy, while not using ionizing radiation. There was no significant difference in surgical time. Residue rates can likely be decreased with specific software functions and surgical tools.
| S-EPMC5957352 | biostudies-literature
Project description:BackgroundAtypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones.MethodsThis retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test.FindingsFive radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617).InterpretationX-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones.FundingAIRC Investigator Grant.
| S-EPMC10884340 | biostudies-literature
Project description:Chondrosarcomas are the most common primary bone malignancy in adults within the United States. Low-grade chondrosarcomas of the long bones, now referred to as atypical cartilaginous tumors (ACTs), have undergone considerable changes in recommended management over the past 20 years, although controversy remains. Diagnostic needle biopsy is recommended only in ambiguous lesions that cannot be clinically diagnosed with a multidisciplinary team. Local excision is preferred due to better functional and equivalent oncologic outcomes. We sought to determine whether these changes are reflected in reported management of ACTs. The National Cancer Database (NCDB) 2004 to 2016 was queried for ACTs of the long bones. Reported patient demographics and tumor clinicopathologic findings were extracted and compared between patients who underwent local excision versus wide resection. We identified 1174 ACT patients in the NCDB. Of these, 586 underwent local excision and 588 underwent wide resection. No significant differences were found in patient demographics. No significant change was found in the reported percentage of diagnostic biopsies or wide resections performed over time. After multivariate regression, the single greatest predictor of performing wide resection on an ACTs was presenting tumor size. Evaluation of the NCDB demonstrated that despite changes in the recommended management of ACTs, there has not been a significant change in surgical treatment over time. Surgeons have been performing diagnostic biopsies and wide resections at similar to historical rates. Persistency of these practices may be due to presenting tumor size, complex anatomic location, uncertainty of underlying tumor grade, or patient choice as part of clinical shared decision making. The authors anticipate that the rate of biopsies and wide resections performed will decrease over time as a result of improvements in advanced imaging and the implementation of recently updated clinical practice guidelines.
| S-EPMC8683228 | biostudies-literature
Project description:OBJECTIVE:To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma. METHODS:We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (n=46) and validation group (n=22). Radiomics features were extracted from T1WI and T2WI-FS sequences of the whole tumor by two radiologists independently and selected by Low Variance, Univariate feature selection, and least absolute shrinkage and selection operator (LASSO). Radiomics models were constructed by multivariate logistic regression analysis based on the features from T1WI and T2WI-FS sequences. The receiver-operating characteristics (ROC) curve and intraclass correlation coefficient (ICC) analyses of the radiomics models and conventional MR imaging were performed to determine their diagnostic accuracy. RESULTS:The ICC value for interreader agreement of the radiomics features ranged from 0.779 to 0.923, which indicated good agreement. Ten and 11 features were selected from the T1WI and T2WI-FS sequences to construct radiomics models, respectively. The areas under the curve (AUCs) of T1WI and T2WI-FS models were 0.990 and 0.925 in training group and 0.915 and 0.855 in the validation group, respectively, showing no significant differences between the two sequence-based models (P>0.05). In all the cases, the AUCs of the two radiomics models based on T1WI and T2WI-FS sequences and conventional MR imaging were 0.955, 0.901 and 0.569, respectively, demonstrating a significantly higher diagnostic accuracy of the two sequence-based radiomics models than conventional MR imaging (P<0.01). CONCLUSIONS:The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.
| S-EPMC7225098 | biostudies-literature
Project description:BackgroundThe differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.Materials and methodsA sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin's tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin's lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.ResultsModels for discriminating between Warthin's and malignant tumors, benign and Warthin's tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.ConclusionsRadiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.
| S-EPMC8111169 | biostudies-literature
Project description:BackgroundCT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters.MethodsWe retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination.ResultsA classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85-0.95 vs. AUC = 0.88, CI = 0.84-0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88-0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67-0.84).ConclusionsIn conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.
| S-EPMC7507285 | biostudies-literature
Project description:It is recognized that IDH mutation negative, low-grade epilepsy associated tumors (LEAT) can show diffuse growth patterns and lack the diagnostic hallmarks of either classical dysembryoplastic neuroepithelial tumors (DNT) or typical ganglioglioma. "Nonspecific or diffuse DNT" and more recently "polymorphous low-grade neuroepithelial tumor of the young" have been terms used for these entities. There are few reports on the MRI recognition of these diffuse glioneuronal tumors (dGNT), which is important in planning the extent of surgical resection. In 27 LEATs T1, T2, FLAIR, and postcontrast T1 MRI were evaluated and the pathology reviewed, including immunostaining for NeuN, CD34, MAP2, and IDH1. Each case was then independently classified by pathology or MRI as simple DNT, complex DNT, or dGNT. There was agreement in 23/27 (85%; Kappa score 0.62; p?<?0.01). In 4 cases, there was discrepancy in the diagnosis of simple versus complex DNT but 100% agreement achieved for dGNT. DNT showed significantly more expansion of the cortex, cystic change and ventricle extension than dGNT. dGNT showed significantly more subcortical T2w hyperintensity and focal cortical atrophy which correlated on pathology with CD34 expression, cortical neuronal loss and white matter rarefaction. There was no distinct cortical dysplasia component identified by MRI or pathology in any case. This study highlights that dGNT can be reliably discriminated on MRI from DNT.
| S-EPMC5939705 | biostudies-literature
Project description:High-grade chondrosarcoma, which has a high incidence of local recurrence and pulmonary metastasis despite surgical resection, is associated with poor prognosis. Therefore, new and effective adjuvant therapies are urgently required for this disease. Gamma-aminobutyric acid (GABA), which acts as a neurotrophic factor during nervous system development, is related to the proliferation and migration of certain cancer cells. The GABAergic system, which is composed of GABA, the GABA-synthesizing enzyme glutamic acid decarboxylase (GAD), and GABA receptors, has an important function in nerve growth and development of neural crest. Therefore, the GABAergic system may play important functional roles in the proliferation of chondrosarcoma cells, which are derived from neural crest cells. We examined the anti-tumor effects of the GABAergic system on a chondrosarcoma cell line.We evaluated the underlying mechanisms of the anti-tumor effects of the GABAergic system, such as the involvement of different signaling pathways, apoptosis, and cell cycle arrest, in the high-grade chondrosarcoma cell line OUMS-27. In addition, we performed whole-cell patch-clamp recordings for Ca2+ currents and evaluated the changes in intracellular Ca2+ concentration via Ca2+ channels, which are related to the GABAB receptor in high-grade chondrosarcoma cells.The GABAB receptor antagonist CGP had anti-tumor effects on high-grade chondrosarcoma cells in a dose-dependent manner. The activities of caspase 3 and caspase 9 were significantly elevated in CGP-treated cells compared to in untreated cells. The activity of caspase 8 did not differ significantly between untreated cells and CGP-treated cells. However, caspase 8 tended to be up-regulated in CGP-treated cells. The GABAB receptor antagonist exhibited anti-tumor effects at the G1/S cell cycle checkpoint and induced apoptosis via dual inhibition of the PI3/Akt/mTOR and MAPK signaling pathways. Furthermore, the changes in intracellular Ca2+ via GABAB receptor-related Ca2+ channels inhibited the proliferation of high-grade chondrosarcoma cells by inducing and modulating apoptotic pathways.The GABAB receptor antagonist may improve the prognosis of high-grade chondrosarcoma by exerting anti-tumor effects via different signaling pathways, apoptosis, cell cycle arrest, and Ca2+ channels in high-grade chondrosarcoma cells.
| S-EPMC5842535 | biostudies-literature
Project description:IntroductionRecent classification of neuroendocrine neoplasms has defined well-differentiated high-grade neuroendocrine tumors (NET G3) as a distinct entity from poorly differentiated neuroendocrine carcinoma. The optimal treatment for NET G3 has not been well-described. This study aimed to evaluate metastatic NET G3 response to different treatment regimens.Materials and methodsThis was a retrospective study of patients with NET G3 within the Mayo Clinic database. Patients' demographics along with treatment characteristics, responses, and survival were assessed. Primary endpoints were progression-free survival (PFS) and overall survival. Secondary endpoints were objective response rate (ORR) and disease control rate (DCR).ResultsTreatment data was available in 30 patients with median age of 59.5 years at diagnosis. The primary tumor was mostly pancreatic (73.3%). Ki-67 index was ≥55% in 26.7% of cases. Treatments included capecitabine + temozolomide (CAPTEM) (n = 20), lutetium 177 DOTATATE (PRRT; n = 10), Platinum-etoposide (EP; n = 8), FOLFOX (n = 7), and everolimus (n = 2). CAPTEM exhibited ORR 35%, DCR 65%, and median PFS 9.4 months (95% confidence interval, 2.96-16.07). Both EP and FOLFOX showed similar radiographic response rates with ORR 25.0% and 28.6%; however, median PFS durations were quite distinct at 2.94 and 13.04 months, respectively. PRRT had ORR of 20%, DCR of 70%, and median PFS of 9.13 months.ConclusionAmong patients with NET G3, CAPTEM was the most commonly used treatment with clinically meaningful efficacy and disease control. FOLFOX or PRRT are other potentially active treatment options. EP has some activity in NET G3, but responses appear to be short-lived. Prospective studies evaluating different treatments effects in patients with NET G3 are needed to determine an optimal treatment strategy.Implications for practiceHigh-grade well-differentiated neuroendocrine tumors (NET G3) are considered a different entity from low-grade NET and neuroendocrine carcinoma in terms of prognosis and management. The oral combination of capecitabine and temozolomide is considered a good option in the management of metastatic NET G3 and may be preferred. FOLFOX is another systemic option with reasonable efficacy. Similar to other well-differentiated neuroendocrine tumors, peptide receptor radionuclide therapy seems to have some efficacy in these tumors.
| S-EPMC8100548 | biostudies-literature