Browsing by Author "Sopta, Jelena P. (24328547800)"

Background: Computational analysis of routinely acquired MRI has potential to improve the tumor chemoresistance prediction and to provide decision support in precision medicine, which may extend patient survival. Most radiomic analytical methods are compatible only with rectangular regions of interest (ROIs) and irregular tumor shape is therefore an important limitation. Furthermore, the currently used analytical methods are not directionally sensitive. Purpose: To implement a tumor analysis that is directionally sensitive and compatible with irregularly shaped ROIs. Study Type: Retrospective. Subjects: A total of 54 patients with histopathologic diagnosis of primary osteosarcoma on tubular long bones and with prechemotherapy MRI. Field Strength/Sequence: A 1.5 T, T2-weighted-short-tau-inversion-recovery-fast-spin-echo. Assessment: A model to explore associations with osteosarcoma chemo-responsiveness included MRI data obtained before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Osteosarcoma morphology was analyzed in the MRI data by calculation of the nondirectional two-dimensional (2D) and directional and nondirectional one-dimensional (1D) Higuchi dimensions (Dh). MAP chemotherapy response was assessed by histopathological necrosis. Statistical Tests: The area under the receiver operating characteristic (ROC) curve (AUC) evaluated the association of the calculated features with the actual chemoresponsiveness, using tumor histopathological necrosis (95%) as the endpoint. Least absolute shrinkage and selection operator (LASSO) machine learning and multivariable regression were used for feature selection. Significance was set at <0.05. Results: The nondirectional 1D Dh reached an AUC of 0.88 in association with the 95% tumor necrosis, while the directional 1D analysis along 180 radial lines significantly improved this association according to the Hanley/McNeil test, reaching an AUC of 0.95. The model defined by variable selection using LASSO reached an AUC of 0.98. The directional analysis showed an optimal predictive range between 90° and 97° and revealed structural osteosarcoma anisotropy manifested by its directionally dependent textural properties. Data Conclusion: Directionally sensitive radiomics had superior predictive performance in comparison to the standard nondirectional image analysis algorithms with AUCs reaching 0.95 and full compatibility with irregularly shaped ROIs. Evidence Level: 3. Technical Efficacy: Stage 1. © 2022 International Society for Magnetic Resonance in Medicine.

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images. © 2017 Djuricic, Radulovic, Sopta, Nikitovic and Miloševic.

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images. © 2017 Djuricic, Radulovic, Sopta, Nikitovic and Miloševic.

Identifying risk factors for fracture occurrence in breast cancer (BC) skeletal metastases (SM) may guide the management of such bone deposits. There is sparse evidence regarding receptor status in SM and their relationship to fracture occurrence. Our study aimed to determine the relationship between estrogen (ER), progesterone (PR) and HER2 receptor status and Ki-67 index and fracture occurrence in SM of BC. Exactly 152 samples of SM of BC obtained from individual patients were evaluated. The status of the aforementioned receptors and Ki67 index were determined in SMs samples. Their expression was compared between SM that did and did not develop a fracture. Ninety-one cases sustained a pathological fracture at the SM site, and 61 did not. Patients who sustained a pathological fracture had a higher rate of PR positivity at their SMs as compared to those with no fracture. There was no significant difference between the two groups concerning ER, HER2+ or Ki67 status. SMs secondary to BC with a fracture are more likely to be PR positive than those with no fracture. Determining the receptor status in SMs may identify high-risk groups for fracture occurrence, and determining the PR status may also guide surgical and hormonal therapy. © 2023 UICC.

Identifying risk factors for fracture occurrence in breast cancer (BC) skeletal metastases (SM) may guide the management of such bone deposits. There is sparse evidence regarding receptor status in SM and their relationship to fracture occurrence. Our study aimed to determine the relationship between estrogen (ER), progesterone (PR) and HER2 receptor status and Ki-67 index and fracture occurrence in SM of BC. Exactly 152 samples of SM of BC obtained from individual patients were evaluated. The status of the aforementioned receptors and Ki67 index were determined in SMs samples. Their expression was compared between SM that did and did not develop a fracture. Ninety-one cases sustained a pathological fracture at the SM site, and 61 did not. Patients who sustained a pathological fracture had a higher rate of PR positivity at their SMs as compared to those with no fracture. There was no significant difference between the two groups concerning ER, HER2+ or Ki67 status. SMs secondary to BC with a fracture are more likely to be PR positive than those with no fracture. Determining the receptor status in SMs may identify high-risk groups for fracture occurrence, and determining the PR status may also guide surgical and hormonal therapy. © 2023 UICC.

Introduction: Chondroblastoma is a rare, benign mesenchymal tumor that can behave local aggressive. It most often occurs in the area of the epiphyses of long tubular bones, whereas the appearance in the region of the head and neck is rare, only few reports were published in literature. The incidence of chondroblastoma of the craniofacial region is about 6.4%. Clinical Report: A 46-year-old patient was hospitalized due to growth and pain in the area of the left half of the face, and a tumor of the left infratemporal fossa was diagnosed by computed tomography. The patient was operated with a preauricular surgical approach, parotidectomy, and complete tumor removal was performed. Pathohistological examination revealed chondroblastoma. Conclusions: Chondroblastoma of the craniofacial region is very rare pathohistological finding, characterized by slow and asymptomatic growth until it reaches the appropriate dimensions, the clinical symptomatology depends on the localization of the tumor. Computed tomography diagnostics provides important information about the location, size of the tumor, invasion of surrounding structures, and significantly contributes to the decision on the appropriate surgical approach. Diagnostic dilemma solving only final pathohistological verification. © 2022 Lippincott Williams and Wilkins. All rights reserved.

Background: Due to the high level of cytogenetic heterogeneity in osteosarcoma, personalized treatment is the promising strategy for the improvement in outcomes. This is currently not possible due to the absence of targeted therapies and reliable predictors for response to induction chemotherapy. Objectives: To investigate the predictive value of computational analysis of osteosarcoma magnetic resonance (MR) images. Patients and Methods: Multifractal analysis was performed on MR images of primary osteosarcoma of long tubular bones prior to OsteoSa induction chemotherapy. A total of 900 images derived from 67 good and poor responder patients were classified and compared to the actual retrospective outcome. Results: Among the six calculated multifractal features, Dqmax exerted the highest predictive value with the prediction accuracy of 74.3%, sensitivity of 72.4% and specificity of 76.2%. The obtained classification accuracy was validated by a ten V-fold split sample cross validation. The area under the curve (AUC) value for the best-performing multifractal Dqmax feature was 0.82 (95% confidence interval, 0.70-0.91). Conclusion: These results suggest for the first time that measuring tumor structure by using multifractal geometry can predict an individual patient response to neoadjuvant cytotoxic therapy. Therefore, it potentially allows precise implementation of alternative treatment options. This predictive approach made use of digital data that is routinely collected but currently still underexploited. © 2018, Iranian Journal of Radiology.