Browsing by Author "Radulovic, Marko (57200831760)"

Background: This is the first reported case of a primary intraosseous angioleiomyoma and the second case of a primary leiomyoma of the rib, irrespective of age. Angioleiomyomas mostly occur in patients of advanced age, in any part of the body, particularly the lower extremities and present as painful, slow-growing nodules in the dermis, subcutaneous fat or deep fascia. Other localizations, especially bone, are considered extremely rare, as well as their occurrence in paediatric patients. Case presentation: A 10-year-old girl was admitted to the orthopaedic surgery department for further assessment of a pain localized in the posterior part of the right hemithorax. After magnetic resonance imaging (MRI) and surgical biopsy, intraosseus angioleiomyoma of the fourth rib was diagnosed by histopathology examination. Atypical costal localization of this type of a benign tumour presents diagnostic difficulty, especially in children. The differential diagnoses included cartilaginous tumours, Ewing sarcoma, fibrous dysplasia, Langerhans cell histiocytosis, intraosseous haemangioma and metastatic tumours. We report a detailed diagnostic procedure including MRI, selective angiography and histopathologic examination. Conclusion: Diagnosis of intraosseous angioleiomyoma is difficult due to the extreme rarity of this tumour and absence of pathognomonic radiological signs. Although very rarely identified in bones and young age group, radiographers and reporting doctors should be aware of this possible angioleiomyoma presentation and supported by the provided detailed diagnostic information. © 2018 The Author(s).

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FDM and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness. © 2021 Future Medicine Ltd.. All rights reserved.

Aim: This study aimed to improve osteosarcoma chemoresponsiveness prediction by optimization of computational analysis of MRIs. Patients & methods: Our retrospective predictive model involved osteosarcoma patients with MRI scans performed before OsteoSa MAP neoadjuvant cytotoxic chemotherapy. Results: We found that several monofractal and multifractal algorithms were able to classify tumors according to their chemoresponsiveness. The predictive clues were defined as morphological complexity, homogeneity and fractality. The monofractal feature CV for Λ(G) provided the best predictive association (area under the ROC curve = 0.88; p <0.001), followed by Y-axis intersection of the regression line for box fractal dimension, r² for FDM and tumor circularity. Conclusion: This is the first full-scale study to indicate that computational analysis of pretreatment MRIs could provide imaging biomarkers for the classification of osteosarcoma according to their chemoresponsiveness. © 2021 Future Medicine Ltd.. All rights reserved.

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.

Aim: Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumor histology structural clues. We thus aimed to improve breast cancer prognosis by fractal analysis of tumor histomorphology. Patients & methods: This retrospective study included 92 breast cancer patients without systemic treatment. Results: Fractal dimension and lacunarity of the breast tumor microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters. Conclusion: Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumor sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for cancer risk prognosis. © 2015 Future Medicine Ltd.

Aim: Research in the field of breast cancer outcome prognosis has been focused on molecular biomarkers, while neglecting the discovery of novel tumor histology structural clues. We thus aimed to improve breast cancer prognosis by fractal analysis of tumor histomorphology. Patients & methods: This retrospective study included 92 breast cancer patients without systemic treatment. Results: Fractal dimension and lacunarity of the breast tumor microscopic histology possess prognostic value comparable to the major clinicopathological prognostic parameters. Conclusion: Fractal analysis was performed for the first time on routinely produced archived pan-tissue stained primary breast tumor sections, indicating its potential for clinical use as a simple and cost-effective prognostic indicator of distant metastasis risk to complement the molecular approaches for cancer risk prognosis. © 2015 Future Medicine Ltd.

(1) Background: This study aimed to develop a machine learning model based on radiomics of pretreatment magnetic resonance imaging (MRI) 3D T2W contrast sequence scans combined with clinical parameters (CP) to predict neoadjuvant chemoradiotherapy (nCRT) response in patients with locally advanced rectal carcinoma (LARC). The study also assessed the impact of radiomics dimensionality on predictive performance. (2) Methods: Seventy-five patients were prospectively enrolled with clinicopathologically confirmed LARC and nCRT before surgery. Tumor properties were assessed by calculating 2141 radiomics features. Least absolute shrinkage selection operator (LASSO) and multivariate regression were used for feature selection. (3) Results: Two predictive models were constructed, one starting from 72 CP and 107 radiomics features, and the other from 72 CP and 1862 radiomics features. The models revealed moderately advantageous impact of increased dimensionality, with their predictive respective AUCs of 0.86 and 0.90 in the entire cohort and 0.84 within validation folds. Both models outperformed the CP-only model (AUC = 0.80) which served as the benchmark for predictive performance without radiomics. (4) Conclusions: Predictive models developed in this study combining pretreatment MRI radiomics and clinicopathological features may potentially provide a routine clinical predictor of chemoradiotherapy responders, enabling clinicians to personalize treatment strategies for rectal carcinoma. © 2024 by the authors.

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.

Aim: Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats. Patients & methods: The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months. Results: Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance. Conclusion: The critical prognostic importance of the grayscale texture is revealed. © 2016 Future Medicine Ltd.

Aim: Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats. Patients & methods: The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months. Results: Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance. Conclusion: The critical prognostic importance of the grayscale texture is revealed. © 2016 Future Medicine Ltd.

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insuffcient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Survival and life quality of breast cancer patients could be improved by more aggressive chemotherapy for those at high metastasis risk and less intense treatments for low-risk patients. Such personalized treatment cannot be currently achieved due to the insuffcient reliability of metastasis risk prognosis. The purpose of this study was therefore, to identify novel histopathological prognostic markers of metastasis risk through exhaustive computational image analysis of 80 size and shape subsets of epithelial clusters in breast tumors. The group of 102 patients had a follow-up median of 12.3 years, without lymph node spread and systemic treatments. Epithelial cells were stained by the AE1/AE3 pan-cytokeratin antibody cocktail. The size and shape subsets of the stained epithelial cell clusters were defined in each image by use of the circularity and size filters and analyzed for prognostic performance. Epithelial areas with the optimal prognostic performance were uniformly small and round and could be recognized as individual epithelial cells scattered in tumor stroma. Their count achieved an area under the receiver operating characteristic curve (AUC) of 0.82, total area (AUC = 0.77), average size (AUC = 0.63), and circularity (AUC = 0.62). In conclusion, by use of computational image analysis as a hypothesis-free discovery tool, this study reveals the histomorphological marker with a high prognostic value that is simple and therefore easy to quantify by visual microscopy. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Background and Objectives: Excessive use of the knee in patients with immature locomotor systems leads to a whole spectrum of morphological changes with possible consequences in adulthood. This study aimed to examine the morphological pattern in magnetic resonance imaging (MRI) that is associated with recurrent pain due to increased physical activity in children. Materials and Methods: This was a retrospective study conducted among pediatric patients treated at the University Children’s Hospital in Belgrade in 2018 and 2019. MRI findings of patients who reported recurrent pain in the knee joint during physical activity and who were without any pathological findings on both clinical examination and knee radiographs were included in the study. Results: MRI findings of 168 patients (73 boys and 95 girls, mean age 14.07 ± 3.34 years) were assessed. Meniscus and cartilage lesions were the most commonly detected morphological findings: meniscus lesions in 49.4%, cartilage ruptures in 44.6%, and cartilage edema in 26.2% of patients. The medial meniscus was more often injured in girls (p = 0.030), while boys were more prone to other joint injuries (p = 0.016), re-injury of the same joint (p = 0.036), bone bruises (p < 0.001), and ligament injuries (p = 0.001). In children older than 15 years, tibial plateau cartilage edema (p = 0.016), chondromalacia patellae (p = 0.005), and retropatellar effusion (p = 0.011) were detected more frequently compared to younger children. Conclusions: Children reporting recurrent knee pain due to increased physical activity, without any detected pathological findings on clinical examination and knee radiography, may have morphological changes that can be detected on MRI. Timely diagnosis of joint lesions should play a significant role in preventing permanent joint dysfunction in the pediatric population as well as in preventing the development of musculoskeletal diseases in adulthood. © 2022 by the authors.

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors. © 2018 Rajkovic, Li, Plataniotis, Kanjer, Radulovic and Miloševic.

Improved prognosis of breast cancer outcome could prolong patient survival by reliable identification of patients at high risk of metastasis occurrence which could benefit from more aggressive treatments. Based on such clinical need, we prognostically evaluated the malignant cells in breast tumors, as the obvious potential source of unexploited prognostic information. The patient group was homogeneous, without any systemic treatments or lymph node spread, with smaller tumor size (pT1/2) and a long follow-up. Epithelial cells were labeled with AE1/AE3 pan-cytokeratin antibody cocktail and comprehensively analyzed. Monofractal and multifractal analyses were applied for quantification of distribution, shape, complexity and texture of malignant cell clusters, while mean pixel intensity and total area were measures of the pan-cytokeratin immunostaining intensity. The results surprisingly indicate that simple binary images and monofractal analysis provided better prognostic information then grayscale images and multifractal analysis. The key findings were that shapes and distribution of malignant cell clusters (by binary fractal dimension; AUC = 0.29), their contour shapes (by outline fractal dimension; AUC = 0.31) and intensity of the pan-cytokeratin immunostaining (by mean pixel intensity; AUC = 0.30) offered significant performance in metastasis risk prognostication. The results reveal an association between the lower pan-cytokeratin staining intensity and the high metastasis risk. Another interesting result was that multivariate analysis could confirm the prognostic independence only for fractal but not for immunostaining intensity features. The obtained results reveal several novel and unexpected findings highlighting the independent prognostic efficacy of malignant cell cluster distribution and contour shapes in breast tumors. © 2018 Rajkovic, Li, Plataniotis, Kanjer, Radulovic and Miloševic.

Background: It is projected that, by 2040, the number of new cases of colorectal cancer (CRC) will increase to 3.2 million, and the number of deaths to 1.6 million, highlighting the need for prevention strategies, early detection and adequate follow-up. In this study, we aimed to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future. Methods: Epidemiological data were derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits. Results: Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) showed a mutation in MMR genes with pathogenic or likely pathogenic significance and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene. Conclusions: Although significant improvements in CRC management have occurred globally in recent years, a strategic approach leading to population-based systemic solutions is required. The high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy will be especially important factors for countries with limited healthcare resources like Serbia. © 2024 by the authors.

Background: It is projected that, by 2040, the number of new cases of colorectal cancer (CRC) will increase to 3.2 million, and the number of deaths to 1.6 million, highlighting the need for prevention strategies, early detection and adequate follow-up. In this study, we aimed to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future. Methods: Epidemiological data were derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits. Results: Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) showed a mutation in MMR genes with pathogenic or likely pathogenic significance and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene. Conclusions: Although significant improvements in CRC management have occurred globally in recent years, a strategic approach leading to population-based systemic solutions is required. The high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy will be especially important factors for countries with limited healthcare resources like Serbia. © 2024 by the authors.