Stromal osseous metaplasia in urothelial carcinoma of the bladder: An unusual and challenging feature. A case report / Carcinoma urotelial de vejiga con metaplasia ósea estromal: una característica inusual y desafiante. Presentación de un caso

A 62-year-old male presented with pain and haematuria starting 3 months before. The computed tomography showed focal and mural bladder thickening with ureteropelvic dilatation. The following transurethral bladder resection revealed a high-grade muscle-invasive urothelial carcinoma. In the subsequent cystoprostatectomy we found the same tumour, but adding focal tumour-associated stromal osseous metaplasia. Ossifying metaplasia is an extremely rare feature in urothelial carcinoma, with a few reported cases and represents a diagnostic challenge, mimicking radiotherapy-induced sarcoma or sarcomatoid carcinoma. (AU)

Varón de 62 años que consulta por dolor y hematuria desde hace 3 meses. En la tomografía computarizada se observó un engrosamiento focal y mural de la vejiga con dilatación ureteropélvica. La resección vesical transuretral reveló un carcinoma urotelial infiltrante de alto grado músculo-invasivo. En la cistoprostatectomía posterior encontramos el mismo tumor, pero añadiendo focos de metaplasia ósea estromal asociada al tumor. La metaplasia osificante es una característica extremadamente rara en el carcinoma urotelial, con algunos casos informados, y representa un desafío diagnóstico, ya que simula un sarcoma inducido por radioterapia o un carcinoma sarcomatoide. (AU)

Clinical significance of intracranial hemorrhage after thrombectomy detected solely by magnetic resonance imaging and not by computed tomography.

BACKGROUND AND OBJECTIVE: Whether intracranial hemorrhage (ICH) detected using magnetic resonance imaging (MRI) affects the clinical outcomes of patients with large-vessel occlusion (LVO) treated with mechanical thrombectomy (MT) remains unclear. This study investigated the clinical features of ICH after MT detected solely by MRI. METHODS: This was a retrospective analysis of patients with acute ischemic stroke and occlusion of the internal carotid artery or middle cerebral artery treated with MT between April 2011 and March 2021. Among 632 patients, patients diagnosed with no ICH using CT, with a pre-morbid modified Rankin Scale (mRS) score ≤ 2, and those who underwent MRI including T2* and computed tomography (CT) within 72 h from MT were enrolled. The main outcomes were the association between ICH detected solely by MRI and clinical outcomes at 90 days. Poor clinical outcomes were defined as mRS score > 2 at 90 days after onset. RESULTS: Of the 246 patients, 29 (12%) had ICH on MRI (MRI-ICH(+)), and 217 (88%) were MRI-ICH(-). There was no significant difference between number of patients with MRI-ICH(+) experiencing poor (10 [12%]) and favorable (19 [12%]) outcomes. The mRS score at 90 days between patients with MRI-ICH (+) and MRI-ICH(-) was not significantly different (2 [1-4] vs. 2 [1-4], respectively). Higher age and lower ASPECTS were independent risk factors for poor outcomes, as shown by multivariate regression analysis. MRI-ICH(+) status was not associated with poor outcomes. CONCLUSIONS: ICH detected by MRI alone did not influence clinical outcomes in patients with LVO treated with MT.

Preoperative three-dimensional lung volumetry predicts respiratory complications in patients undergoing major liver resection for colorectal metastases.

Colorectal liver metastases (CRLM) are the predominant factor limiting survival in patients with colorectal cancer and liver resection with complete tumor removal is the best treatment option for these patients. This study examines the predictive ability of three-dimensional lung volumetry (3DLV) based on preoperative computerized tomography (CT), to predict postoperative pulmonary complications in patients undergoing major liver resection for CRLM. Patients undergoing major curative liver resection for CRLM between 2010 and 2021 with a preoperative CT scan of the thorax within 6 weeks of surgery, were included. Total lung volume (TLV) was calculated using volumetry software 3D-Slicer version 4.11.20210226 including Chest Imaging Platform extension ( http://www.slicer.org ). The area under the curve (AUC) of a receiver-operating characteristic analysis was used to define a cut-off value of TLV, for predicting the occurrence of postoperative respiratory complications. Differences between patients with TLV below and above the cut-off were examined with Chi-square or Fisher's exact test and Mann-Whitney U tests and logistic regression was used to determine independent risk factors for the development of respiratory complications. A total of 123 patients were included, of which 35 (29%) developed respiratory complications. A predictive ability of TLV regarding respiratory complications was shown (AUC 0.62, p = 0.036) and a cut-off value of 4500 cm3 was defined. Patients with TLV < 4500 cm3 were shown to suffer from significantly higher rates of respiratory complications (44% vs. 21%, p = 0.007) compared to the rest. Logistic regression analysis identified TLV < 4500 cm3 as an independent predictor for the occurrence of respiratory complications (odds ratio 3.777, 95% confidence intervals 1.488-9.588, p = 0.005). Preoperative 3DLV is a viable technique for prediction of postoperative pulmonary complications in patients undergoing major liver resection for CRLM. More studies in larger cohorts are necessary to further evaluate this technique.

Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.

BACKGROUND: Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning. We used this IDP to classify individuals with metabolic syndrome and predict future occurrence of cardiometabolic disorders. METHODS: A multi-stage deep learning approach was used to extract the IDP from the liver region of unenhanced abdominal CT scans. In a cohort of over 2,000 individuals the IDP was used to classify individuals with metabolic syndrome. In a subset of over 1,300 individuals, the IDP was used to predict future occurrence of hypertension, type II diabetes, and fatty liver disease. RESULTS: For metabolic syndrome (MetS) classification, we compared the performance of the proposed IDP to liver attenuation and visceral adipose tissue area (VAT). The proposed IDP showed the strongest performance (AUC 0.82) compared to attenuation (AUC 0.70) and VAT (AUC 0.80). For disease prediction, we compared the performance of the IDP to baseline MetS diagnosis. The models including the IDP outperformed MetS for type II diabetes (AUCs 0.91 and 0.90) and fatty liver disease (AUCs 0.67 and 0.62) prediction and performed comparably for hypertension prediction (AUCs of 0.77). CONCLUSIONS: This study demonstrated the superior performance of a deep learning IDP compared to traditional radiomic features to classify individuals with metabolic syndrome. Additionally, the IDP outperformed the clinical definition of metabolic syndrome in predicting future morbidities. Our findings underscore the utility of data-driven imaging phenotypes as valuable tools in the assessment and management of metabolic syndrome and cardiometabolic disorders.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Conservative Management of Large Post-Intubation Tracheal Laceration.

ABSTRACT: Tracheal rupture is a very rare but life-threatening complication of endotracheal intubation. It is more common in women and patients older than 50 years old. Overinflation of endotracheal tube cuff and tracheal wall weakening are the most important pathogenetic mechanisms. Laceration sites are located in the posterior membranous wall in most cases. Subcutaneous and mediastinal emphysema and respiratory distress are the most common manifestations. A 55-year-old female presented with postoperative subcutaneous and mediastinal emphysema without dyspnea because of a tear in the posterior tracheal wall. The diagnosis was based on clinical manifestation, chest computer tomography scans (CT), and endoscopic findings. A conservative approach by broad-spectrum antibiotic therapy was decided because of patients' vital signs stability and the absence of esophageal injury. The follow-up showed that there was no lesion in the posterior tracheal wall. Our case showed that in clinically stable patients without mediastinitis and with spontaneous breathing, conservative management of tracheal tears is a safe procedure.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Clinical usability of 3D gradient-echo-based ultrashort echo time imaging: Is it enough to facilitate diagnostic decision in real-world practice?

BACKGROUND: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). METHODS: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. RESULTS: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). CONCLUSIONS: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules.

Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.

OBJECTIVE: To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging. MATERIALS AND METHODS: A training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1-8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation. RESULTS: For "brain compression", a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For "brain edema", a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for "brain compression," the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For "brain edema," the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation. DISCUSSION: A natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports. CONCLUSION: This result may justify the use of machine learning-based decision support to augment provider documentation.

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors.

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

Automated segmentation and volume prediction in pediatric Wilms' tumor CT using nnu-net.

BACKGROUND: Radiologic volumetric evaluation of Wilms' tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning. METHODS: We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment. RESULTS: The DSC and HD95 was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm3 and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm3 and 10.16% ± 9.70%. CONCLUSION: We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Successful bilateral lung transplantation in pulmonary alveolar microlithiasis: A case report and review of literature.

BACKGROUND: Pulmonary alveolar microlithiasis (PAM) is a rare autosomal recessive genetic disorder with approximately 1000 known cases worldwide, in which calcium phosphate microliths deposit in the alveolar air spaces. As of writing this report, no definitive conventional therapy exists, and many PAM cases may progress to severe respiratory failure and potential death. Bilateral lung transplantation (BLx) seems to be the most optimal solution; however, this procedure is challenging along with limited reports regarding the outcome in PAM. We report a case of PAM successfully treated with BLx for the first time in Iran. METHOD: We present the case of a 42-year-old female with a longstanding history of cough, not responding to conventional antitussive medication, who was diagnosed as a case of PAM following a hospitalization due to coughing, dyspnea on exertion, and hemoptysis. Despite treatment with corticosteroid and medical treatment, no improvement was achieved and she subsequently developed respiratory and right ventricular failure, with oxygen ventilation dependence. Eventually, she was scheduled for BLx. The operation was successful and during her 2-year follow-up, no recurrence or significant postoperative complications has been reported. CONCLUSION: This case presentation and literature review confirm the effectiveness of BLx as a promising treatment for PAM-diagnosed patients, improving both life expectancy and quality of life.

Cochlear Implantation in Patients With Osteolytic Labyrinthitis: A Case Series.

OBJECTIVE: To describe the rare process of osteolytic labyrinthitis, previously referred to as labyrinthine sequestrum, which involves progressive obliteration of the bony and membranous labyrinth with eventual supplantation with soft tissue and, in some cases, bony sequestrum. PATIENTS: Three patients with diverse presentations of osteolytic labyrinthitis from two tertiary care academic medical centers. INTERVENTIONS: Case series report analyzing the relevant clinical, radiologic, pathologic, and surgical data on our patients with osteolytic labyrinthitis and comparing these index cases to the existing literature. MAIN OUTCOME MEASURES: We describe the varying image findings seen in osteolytic labyrinthitis on computed tomography and magnetic resonance imaging. Also, we report successful surgical intervention and hearing rehabilitation with cochlear implantation in patients with osteolytic labyrinthitis. RESULTS: Our three patients presented with profound sudden sensorineural hearing loss and vertigo consistent with labyrinthitis. None of the three patients had a history of chronic otitis media. Imaging workup revealed varying degrees of erosion to the otic capsule bone demonstrating the spectrum of disease seen in osteolytic labyrinthitis. Although two cases showed osteolytic changes to the semicircular canals and vestibule, the first case revealed frank bony sequestrum within the obliterated labyrinth. The three cases were taken for surgical debridement and cochlear implantation. CONCLUSIONS: We propose the new term, osteolytic labyrinthitis-previously referred to as labyrinthine sequestrum-to describe the rare spectrum of disease characterized by destruction of the osseous and membranous labyrinth and potential supplantation with bony sequestrum. Cochlear implantation is a viable option in selected patients with osteolytic labyrinthitis.

Intrabiliary Rupture of a Hepatic Hydatid Cyst: A Case Report.

BACKGROUND Hydatid disease is a common parasitic infection in many areas of Asia, South America, and Africa. It can affect any organ, most commonly the liver. The hydatid is often asymptomatic and the diagnosis is made when complications arise. The most common complication of this disease is opening in the bile ducts, which is a life-threatening condition causing serious acute cholangitis. We report a case of acute cholangitis caused by hydatid cyst rupture into the right bile duct. CASE REPORT A 33-year-old woman, with no medical or surgical history, presented to our Emergency Department with abdominal pain, jaundice, and fever for 3 days prior to admission. The patient was hemodynamically stable. In the examination, we noticed right upper-quadrant tenderness with guarding, icterus sclera, and negative Murphy sign. A CT scan showed a liver hydatid cyst of the 4th and 8th of segments, with intrahepatic and extrahepatic biliary duct dilation. The cyst communicated with the right hepatic bile duct via a large fistula. A diagnosis of acute cholangitis was made and she underwent conservative treatment with external drainage of the pericystic cavity through the biliary duct. The postoperative course was uncomplicated and she was discharged 15 days later. CONCLUSIONS The surgical approach to hepatic hydatid must be customized based on the specific characteristics of the cyst and associated complications. Acute hydatid cholangitis is a rare but serious complication of a hydatid cyst, which requires early diagnosis and adequate surgical management.

[Osteosarcoma Secondary to Polyostotoic Fibrous Dysplasia of the Ribs].

Sarcomatous transformation of fibrous dysplasia is extremely rare. We present the case of a 54-yearold man with multiple rib masses, multiple enlarged lymph nodes throughout the body, and multiple osteolytic lesions on computed tomography( CT). A positron emission tomography( PET) scan showed abnormal enhancement in each. A needle biopsy of the right supraclavicular fossa lymph node revealed sarcoidosis. Considering the possibility of malignancy associated with sarcoidosis, a rib tumor resection and mediastinal lymph node biopsy were performed to confirm the diagnosis of the rib lesion. The pathology results showed that the rib mass was a low-grade central osteosarcoma and the mediastinal lymph node was sarcoidosis. The distribution of the lesions was consistent with osteosarcoma secondary to multiple fibrous bone dysplasia. As the osteosarcoma was low grade, the patient was followed up. Three years after surgery, there was no increase in residual disease.