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Background: The diagnostic work-up of musculoskeletal tumors is a multifactorial process. During the early phase, differential diagnoses are made using basic radiological imaging. In this phase, part of the decision making is based on the patient's age, as well as the incidence and predilection sites of different entities. Unfortunately, this information is based on older and fragmented data. In this study, we retrospectively evaluated all soft-tissue and bone tumors around the knee in children treated at our tertiary center in the last 20 years, with the aim of verifying the data used today. Methods: In this retrospective study, the databank of our tertiary center was used to give an overview of treated tumors around the knee in children. Results: We were able to include 224 children with bone and soft-tissue tumors around the knee. The cohort consisted of 184 bone tumors, of which 144 were benign and 40 malignant. The 40 soft-tissue tumors comprised 30 benign and 10 malignant masses. The most common lesions were osteochondromas (88) in the bone and tenosynovial giant-cell tumors (12) in the soft tissue. Conclusions: With this original work, we were able to verify and supplement earlier studies, as well as deepen our insight into these very rare diseases.
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BACKGROUND: An aberrant cellular microenvironment characterized by pathological cells or inflammation represents an added risk factor across various cancer types. While the significance of chronic inflammation in the development of most diffuse tumors has been extensively studied, an exception to this analysis exists in the context of chondrosarcomas. Chondrosarcomas account for 20-30% of all bone sarcomas, with an estimated global incidence of 1 in 100,000. The average age at diagnosis is 50, and over 70% of patients are over 40. This retrospective study aimed to examine the role of C-reactive protein (CRP) as a prognostic factor in relation to the histopathological findings in chondrosarcoma. METHODS: In this retrospective study, 70 patients diagnosed with chondrosarcoma and treated between 2004 and 2019 were included. Preoperative CRP levels were measured in mg/dL, with non-pathological values defined as below 0.5 mg/dL. Disease-free survival time was calculated from the initial diagnosis to events such as local recurrence or metastasis. Follow-up status was categorized as death from disease, no evidence of disease, or alive with disease. Patients were excluded if they had insufficient laboratory values, missing follow-up information, or incomplete histopathological reports. RESULTS: The calculated risk estimation of a reduced follow-up time was 2.25 timed higher in the patients with a CRP level >0.5 mg/dL (HR 2.25 and 95% CI 1.13-4.45) and 3 times higher in patients with a tumor size > pT2 (HR 3 and 95% CI 1.59-5.92). We can easily confirm that risk factors for reduced prognosis lie in chondrosarcoma high grading, preoperative pathological CRP- level, and a size > 8 cm. CONCLUSIONS: A pretreatment CRP value greater than 0.5 mg/dL can be considered a sensitive prognostic and risk factor for distant metastasis for chondrosarcoma patients.
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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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Algoritmos , Benchmarking , Aprendizado Profundo , Extremidades , Imageamento por Ressonância Magnética , Sarcoma , Humanos , Sarcoma/diagnóstico por imagem , Sarcoma/radioterapia , Sarcoma/patologia , Imageamento por Ressonância Magnética/métodos , Masculino , Feminino , Extremidades/diagnóstico por imagem , Pessoa de Meia-Idade , Adulto , Idoso , Planejamento da Radioterapia Assistida por Computador/métodos , Neoplasias de Tecidos Moles/diagnóstico por imagem , Neoplasias de Tecidos Moles/radioterapia , Neoplasias de Tecidos Moles/patologia , RadiômicaRESUMO
Undifferentiated small round cell sarcomas (USRS) of bone and soft tissue are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n = 42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n = 24) or EWSR1::ERG fusions (n = 4), sarcomas with an EWSR1 rearrangement (n = 2), CIC::DUX4 fusion (n = 8), as well as tumors classified as USRS with no genetic data available (n = 4). Proteins extracted from formalin-fixed, paraffin-embedded pretherapeutic biopsies were analyzed qualitatively and quantitatively using shotgun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in 2 cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas, and BACH2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study, and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expressions of these 3 markers in the 2 genetic groups were further validated in an independent cohort of n = 34 USRS. Finally, our proteomic results point toward diverging signaling pathways in the different USRS subgroups.
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Biomarcadores Tumorais , Proteômica , Proteína EWS de Ligação a RNA , Sarcoma de Células Pequenas , Translocação Genética , Humanos , Proteína EWS de Ligação a RNA/genética , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/análise , Sarcoma de Células Pequenas/genética , Sarcoma de Células Pequenas/patologia , Sarcoma de Células Pequenas/diagnóstico , Feminino , Masculino , Adulto , Adolescente , Adulto Jovem , Neoplasias de Tecidos Moles/genética , Neoplasias de Tecidos Moles/patologia , Neoplasias de Tecidos Moles/diagnóstico , Pessoa de Meia-Idade , Proteínas de Fusão Oncogênica/genética , Neoplasias Ósseas/genética , Neoplasias Ósseas/patologia , Neoplasias Ósseas/diagnóstico , Criança , Proteínas de Ligação a Calmodulina/genética , Proteínas de Ligação a RNA/genética , Proteínas Repressoras/genéticaRESUMO
Primary leiomyosarcoma of bone (LMSoB) is extremely rare, comprising only <0.7% of primary malignant bone tumors, and is therefore considered an ultra-rare tumor entity. There is currently no consensus as to whether therapeutic strategies should be based on the biological characteristics of soft tissue leiomyosarcoma or on primary tumor localization in the bone. The use of perioperative chemotherapy and its effectiveness in this rare tumor entity remains unclear. We aimed to evaluate the impact of different treatment approaches in a multicenter setting with a total of 35 patients included. The 5-year overall survival (OS) was 74%. Patients with localized disease undergoing surgery had a significantly higher 5-year OS compared to patients who did not undergo surgical treatment (82% vs. 0%, p = 0.0015). Axial tumor localization was associated with worse event-free survival (EFS) probability (p < 0.001) and OS (p = 0.0082). A high proportion of our patients developed secondary metastases. Furthermore, the perioperative chemotherapy protocols applied to our patients were not associated with an improved EFS or OS. Therefore, the benefit of perioperative chemotherapy in LMSoB needs to be further investigated, and the choice of agents still needs to be clarified.
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BACKGROUND: Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment. METHODS: We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation. RESULTS: We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002). CONCLUSIONS: Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible. RELEVANCE STATEMENT: 3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics. KEY POINTS: ⢠Matrix calcifications are a relevant diagnostic feature of bone tumors. ⢠Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. ⢠Micro-CT has the potential to be an integrative part of clinical diagnostics.
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Neoplasias Ósseas , Condrossarcoma , Estudos de Viabilidade , Imageamento Tridimensional , Microtomografia por Raio-X , Humanos , Condrossarcoma/diagnóstico por imagem , Condrossarcoma/patologia , Microtomografia por Raio-X/métodos , Idoso , Neoplasias Ósseas/diagnóstico por imagem , Neoplasias Ósseas/patologia , Pessoa de Meia-Idade , Estudos Retrospectivos , Imageamento Tridimensional/métodos , Masculino , Feminino , Coloração e Rotulagem/métodosRESUMO
Overweight patients have higher complication rates during and after surgical procedures. In total hip arthroplasty (THA), postoperative infection is a major complication. In this study, we show that the patient's body mass index (BMI) can be approximated by a newly developed grading system using preoperative X-rays. Furthermore, we show that a higher score and BMI result in a higher risk of infection. For this retrospective study, 635 patients undergoing THA or revision surgeries in 2018 and 2019 were included. The preoperatively acquired X-rays of the pelvis were analyzed using a four-stage grading system. The infection rate was compared to our score and the patients' BMI. The mean BMI (95% confidence) of all patients graded as grade 0 was 25.16 (24.83; 25.50) kg/m2, for grade 1, it was 30.31 (29.52; 31.09) kg/m2, for grade 2, it was 35.06 (33.59; 36.54) kg/m2, and it was 45.03 (39.65; 50.41) kg/m2 for grade 3. The risk of infection was 4% in patients with normal radiographs, rising from 7% in patients graded as 1 up to 18% in each of the highest categories. This study shows that we were able to create a semi-quantitative grading tool for the abdominal contour displayed on X-rays of the pelvis in order to estimate the patients' BMI and therefore the infection rate. A higher abdominal contour grade showed higher infection rates at follow-up.
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PURPOSE: To assess diagnostic delay in patients with osteoid osteoma and to analyze influencing factors. MATERIALS AND METHODS: All patients treated for osteoid osteoma at our tertiary referral center between December 1997 and February 2021 were retrospectively identified (nâ=â302). The diagnosis was verified by an expert panel of radiologists and orthopedic surgeons. The exclusion criteria were post-interventional recurrence, missing data on symptom onset, and lack of pretherapeutic CT images. Clinical parameters were retrieved from the local clinical information system. CT and MR images were assessed by a senior specialist in musculoskeletal radiology. RESULTS: After all exclusions, we studied 162 patients (mean age: 24â±â11 years, 115 men). The average diagnostic delay was 419â±â485 days (median: 275 days; range: 21-4503 days). Gender, patient age, presence of nocturnal pain, positive aspirin test, extent of bone sclerosis, and location of the tumor within bone and relative to joints did not influence diagnostic delay (pâ>â0.05). It was, however, positively correlated with nidus size (râ=â0.26; pâ<â0.001) and was shorter with affection of long tubular bones compared to all other sites (pâ=â0.04). If osteoid osteoma was included in the initial differential diagnoses, the diagnostic delay was also shorter (pâ=â0.007). CONCLUSION: The diagnostic delay in patients with osteoid osteoma is independent of demographics, clinical parameters, and most imaging parameters. A long average delay of more than one year suggests low awareness of the disease among physicians. Patients with unclear imaging findings should thus be referred to a specialized musculoskeletal center or an expert in the field should be consulted in a timely manner. KEY POINTS: · In this retrospective study of 162 patients treated for osteoid osteoma, the median diagnostic delay was 275 days (range: 21-4503 days).. · Gender, age, presence of nocturnal pain, positive aspirin test, extent of bone sclerosis, and location of the tumor did not influence the diagnostic delay (pâ>â0.05).. · Diagnostic delay was positively correlated with nidus size (râ=â0.26; pâ<â0.001) and was shorter with affection of long tubular bones compared to all other sites (376â±â485 vs. 560â±â462 days; pâ=â0.04)..
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Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.
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BACKGROUND: The aim of this study was to develop and validate radiogenomic models to predict the MDM2 gene amplification status and differentiate between ALTs and lipomas on preoperative MR images. METHODS: MR images were obtained in 257 patients diagnosed with ALTs (n = 65) or lipomas (n = 192) using histology and the MDM2 gene analysis as a reference standard. The protocols included T2-, T1-, and fat-suppressed contrast-enhanced T1-weighted sequences. Additionally, 50 patients were obtained from a different hospital for external testing. Radiomic features were selected using mRMR. Using repeated nested cross-validation, the machine-learning models were trained on radiomic features and demographic information. For comparison, the external test set was evaluated by three radiology residents and one attending radiologist. RESULTS: A LASSO classifier trained on radiomic features from all sequences performed best, with an AUC of 0.88, 70% sensitivity, 81% specificity, and 76% accuracy. In comparison, the radiology residents achieved 60-70% accuracy, 55-80% sensitivity, and 63-77% specificity, while the attending radiologist achieved 90% accuracy, 96% sensitivity, and 87% specificity. CONCLUSION: A radiogenomic model combining features from multiple MR sequences showed the best performance in predicting the MDM2 gene amplification status. The model showed a higher accuracy compared to the radiology residents, though lower compared to the attending radiologist.
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BACKGROUND: Soft tissue sarcomas (STS) are a relatively rare group of malignant tumors. Currently, there is very little published clinical data, especially in the context of curative multimodal therapy with image-guided, conformal, intensity-modulated radiotherapy. METHODS: Patients who received preoperative or postoperative intensity-modulated radiotherapy for STS of the extremities or trunk with curative intent were included in this single centre retrospective analysis. A Kaplan-Meier analysis was performed to evaluate survival endpoints. Multivariable proportional hazard models were used to investigate the association between survival endpoints and tumour-, patient-, and treatment-specific characteristics. RESULTS: 86 patients were included in the analysis. The most common histological subtypes were undifferentiated pleomorphic high-grade sarcoma (UPS) (27) and liposarcoma (22). More than two third of the patients received preoperative radiation therapy (72%). During the follow-up period, 39 patients (45%) suffered from some type of relapse, mainly remote (31%). The two-years overall survival rate was 88%. The median DFS was 48 months and the median DMFS was 51 months. Female gender (HR 0.460 (0.217; 0.973)) and histology of liposarcomas compared to UPS proved to be significantly more favorable in terms of DFS (HR 0.327 (0.126; 0.852)). CONCLUSION: Conformal, intensity-modulated radiotherapy is an effective treatment modality in the preoperative or postoperative management of STS. Especially for the prevention of distant metastases, the establishment of modern systemic therapies or multimodal therapy approaches is necessary.
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Lipossarcoma , Radioterapia de Intensidade Modulada , Sarcoma , Neoplasias de Tecidos Moles , Humanos , Feminino , Estudos Retrospectivos , Recidiva Local de Neoplasia , Adjuvantes Imunológicos , ExtremidadesRESUMO
Background: A pathological/inflamed cellular microenvironment state is an additional risk factor for any cancer type. The importance of a chronic inflammation state in most diffuse types of tumour has already been analysed, except for in Ewing's sarcoma. It is a highly malignant blue round cell tumour, with 90% of cases occurring in patients aged between 5 and 25 years. Worldwide, 2.9 out of 1,000,000 children per year are affected by this malignancy. The aim of this retrospective study was to analyse the role of C-reactive protein (CRP) as a prognostic factor for Ewing's sarcomas. Methods: This retrospective study at Klinikum rechts der Isar included 82 patients with a confirmed Ewing's sarcoma diagnosis treated between 2004 and 2019. Preoperative CRP determination was assessed in mg/dL with a normal value established as below 0.5 mg/dL. Disease-free survival time was calculated as the time between the initial diagnosis and an event such as local recurrence or metastasis. Follow-up status was described as death of disease (DOD), no evidence of disease (NED) or alive with disease (AWD). The exclusion criteria of this study included insufficient laboratory values and a lack of information regarding the follow-up status or non-oncological resection. Results: Serum CRP levels were significantly different in patients with a poorer prognosis (DOD) and in patients who presented distant metastasis (p = 0.0016 and p = 0.009, respectively), whereas CRP levels were not significantly different in patients with local recurrence (p = 0.02). The optimal breakpoint that predicted prognosis was 0.5 mg/dL, with a sensitivity of 0.76 and a specificity of 0.74 (AUC 0.81). Univariate CRP analysis level >0.5 mg/dL revealed a hazard ratio of 9.5 (95% CI 3.5−25.5). Conclusions: In Ewing's sarcoma cases, we consider a CRP pretreatment value >0.5 mg/dL as a sensitive prognostic risk factor indication for distant metastasis and poor prognosis. Further research with more data is required to determine more sensitive cutoff levels.
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The differentiation between the atypical cartilaginous tumor (ACT) and the enchondromas is crucial as ACTs require a curettage and clinical as well as imaging follow-ups, whereas in the majority of cases enchondromas require neither a treatment nor follow-ups. Differentiating enchondromas from ACTs radiologically remains challenging. Therefore, this study evaluated imaging criteria in a combination of computed tomography (CT) and magnetic resonance (MR) imaging for the differentiation between enchondromas and ACTs in long bones. A total of 82 patients who presented consecutively at our institution with either an ACT (23, age 52.7 ±18.8 years; 14 women) or an enchondroma (59, age 46.0 ± 11.1 years; 37 women) over a period of 10 years, who had undergone preoperative MR and CT imaging and subsequent biopsy or/and surgical removal, were included in this study. A histopathological diagnosis was available in all cases. Two experienced radiologists evaluated several imaging criteria on CT and MR images. Likelihood of an ACT was significantly increased if either edema within the bone (p = 0.049), within the adjacent soft tissue (p = 0.006) or continuous growth pattern (p = 0.077) were present or if the fat entrapment (p = 0.027) was absent on MR images. Analyzing imaging features on CT, the likelihood of the diagnosis of an ACT was significantly increased if endosteal scalloping >2/3 (p < 0.001), cortical penetration (p < 0.001) and expansion of bone (p = 0.002) were present and if matrix calcifications were observed in less than 1/3 of the tumor (p = 0.013). All other imaging criteria evaluated showed no significant influence on likelihood of ACT or enchondroma (p > 0.05). In conclusion, both CT and MR imaging show suggestive signs which can help to adequately differentiate enchondromas from ACTs in long bones and therefore can improve diagnostics and consequently patient management. Nevertheless, these features are rare and a combination of CT and MR imaging features did not improve the diagnostic performance substantially.
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BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma. MATERIALS AND METHODS: A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively. RESULTS: The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making. CONCLUSION: Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm was able to cope with very limited data. However, a systematic and structured data acquisition is necessary to further develop the algorithm and increase results to clinical relevance.
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Neoplasias Ósseas , Aprendizado Profundo , Osteomielite , Sarcoma de Ewing , Algoritmos , Inteligência Artificial , Neoplasias Ósseas/diagnóstico por imagem , Neoplasias Ósseas/patologia , Criança , Humanos , Osteomielite/diagnóstico por imagem , Osteomielite/patologia , Estudos Retrospectivos , Sarcoma de Ewing/diagnóstico por imagem , Sarcoma de Ewing/patologiaRESUMO
In this study, we sought to determine the prognostic value of both the European Organization for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (EORTC-STBSG) score and the histologic parameters viable tumor, coagulative necrosis, hyalinization/fibrosis, and infarction in patients (n=64) with localized, nonmetastatic high-grade soft-tissue sarcomas after preoperative radiomonotherapy. A standardized macroscopic workup for pretreated surgical specimen including evaluation of a whole section of high-grade soft tissue sarcomas in the largest diameter, was used. Association with overall survival and disease-free survival was assessed. Limb salvage could be accomplished in 98.4% of patients. Overall, 90.6% tumors had negative resection margins. The median postoperative tumor diameter was 9 cm. Undifferentiated pleomorphic sarcoma (42.2%) and myxofibrosarcoma (17.2%) were the most common diagnoses. In all, 9.4% of patients had local recurrence despite clear resection margins, and 50% had distant metastases. Morphologic mapping suggests an overall heterogenous intratumoral response to radiotherapy, with significant differences among histologic subtypes. Complete regression (0% vital tumor cells) was not seen. Categorizing the results according to the proposed EORTC-STBSG 5-tier response score, <1% viable tumor cells were seen in 3.1%, ≥1% to <10% viable tumor cells in 20.4%, ≥10% to <50% viable tumor cells in 35.9% and ≥50% viable tumor cells in 40.6% of cases. Mean values for viable tumor cells were 40% (range: 1% to 100%), coagulative necrosis 5% (0% to 60%), hyalinization/fibrosis 25% (0% to 90%) and infarction 15% (0% to 79%). Hyalinization/fibrosis was a significant independent prognostic factor for overall survival (hazard ratio=4.4; P =0.047), while the other histologic parameters including the EORTC-STBSG score were not prognostic.
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Sarcoma , Neoplasias de Tecidos Moles , Adulto , Fibrose , Humanos , Infarto , Margens de Excisão , Necrose , Terapia Neoadjuvante/métodos , Recidiva Local de Neoplasia , Estudos Retrospectivos , Sarcoma/patologia , Sarcoma/radioterapia , Neoplasias de Tecidos Moles/patologia , Neoplasias de Tecidos Moles/radioterapiaRESUMO
OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: ⢠The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. ⢠Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. ⢠An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.
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Neoplasias Ósseas , Aprendizado de Máquina , Adolescente , Adulto , Neoplasias Ósseas/diagnóstico por imagem , Feminino , Humanos , Pessoa de Meia-Idade , Radiografia , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Raios X , Adulto JovemRESUMO
OBJECTIVES: To evaluate the performance and reproducibility of MR imaging features in the diagnosis of joint invasion (JI) by malignant bone tumors. METHODS: MR images of patients with and without JI (n = 24 each), who underwent surgical resection at our institution, were read by three radiologists. Direct (intrasynovial tumor tissue (ITT), intraarticular destruction of cartilage/bone, invasion of capsular/ligamentous insertions) and indirect (tumor size, signal alterations of epiphyseal/transarticular bone (bone marrow replacement/edema-like), synovial contrast enhancement, joint effusion) signs of JI were assessed. Odds ratios, sensitivity, specificity, PPV, NPV, and reproducibilities (Cohen's and Fleiss' κ) were calculated for each feature. Moreover, the diagnostic performance of combinations of direct features was assessed. RESULTS: Forty-eight patients (28.7 ± 21.4 years, 26 men) were evaluated. All readers reliably assessed the presence of JI (sensitivity = 92-100 %; specificity = 88-100%, respectively). Best predictors for JI were direct visualization of ITT (OR = 186-229, p < 0.001) and destruction of intraarticular bone (69-324, p < 0.001). Direct visualization of ITT was also highly reliable in assessing JI (sensitivity, specificity, PPV, NPV = 92-100 %), with excellent reproducibility (κ = 0.83). Epiphyseal bone marrow replacement and synovial contrast enhancement were the most sensitive indirect signs, but lacked specificity (29-54%). By combining direct signs with high specificity, sensitivity was increased (96 %) and specificity (100 %) was maintained. CONCLUSION: JI by malignant bone tumors can reliably be assessed on preoperative MR images with high sensitivity, specificity, and reproducibility. Particularly direct visualization of ITT, destruction of intraarticular bone, and a combination of highly specific direct signs were valuable, while indirect signs were less predictive and specific. KEY POINTS: ⢠Direct visualization of intrasynovial tumor was the single most sensitive and specific (92-100%) MR imaging sign of joint invasion. ⢠Indirect signs of joint invasion, such as joint effusion or synovial enhancement, were less sensitive and specific compared to direct signs. ⢠A combination of the most specific direct signs of joint invasion showed best results with perfect specificity and PPV (both 100%) and excellent sensitivity and NPV (both 96 %).
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Neoplasias Ósseas , Neoplasias Ósseas/diagnóstico , Humanos , Ligamentos Articulares/patologia , Imageamento por Ressonância Magnética/métodos , Masculino , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.
Assuntos
Neoplasias Ósseas/diagnóstico por imagem , Aprendizado Profundo , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia/métodos , Adulto , Osso e Ossos/diagnóstico por imagem , Feminino , Humanos , Masculino , Estudos RetrospectivosRESUMO
PURPOSE: In high-grade soft-tissue sarcomas (STS) the standard of care encompasses multimodal therapy regimens. While there is a growing body of evidence for prognostic pretreatment radiomic models, we hypothesized that temporal changes in radiomic features following neoadjuvant treatment ("delta-radiomics") may be able to predict the pathological complete response (pCR). METHODS: MRI scans (T1-weighted with fat-saturation and contrast-enhancement (T1FSGd) and T2-weighted with fat-saturation (T2FS)) of patients with STS of the extremities and trunk treated with neoadjuvant therapy were gathered from two independent institutions (training: 103, external testing: 53 patients). pCR was defined as <5% viable cells. After segmentation and preprocessing, 105 radiomic features were extracted. Delta-radiomic features were calculated by subtraction of features derived from MRI scans obtained before and after neoadjuvant therapy. After feature reduction, machine learning modeling was performed in 100 iterations of 3-fold nested cross-validation. Delta-radiomic models were compared with single timepoint models in the testing cohort. RESULTS: The combined delta-radiomic models achieved the best area under the receiver operating characteristic curve (AUC) of 0.75. Pre-therapeutic tumor volume was the best conventional predictor (AUC 0.70). The T2FS-based delta-radiomic model had the most balanced classification performance with a balanced accuracy of 0.69. Delta-radiomic models achieved better reproducibility than single timepoint radiomic models, RECIST or the peri-therapeutic volume change. Delta-radiomic models were significantly associated with survival in multivariate Cox regression. CONCLUSION: This exploratory analysis demonstrated that MRI-based delta-radiomics improves prediction of pCR over tumor volume and RECIST. Delta-radiomics may one day function as a biomarker for personalized treatment adaptations.
Assuntos
Terapia Neoadjuvante , Sarcoma , Humanos , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Reprodutibilidade dos Testes , Estudos Retrospectivos , Sarcoma/diagnóstico por imagem , Sarcoma/terapiaRESUMO
BACKGROUND: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. METHODS: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. RESULTS: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. CONCLUSIONS: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.