Primary Ewing sarcoma of the kidney mimicking cystic papillary renal cell carcinoma in an older patient: A case report.

BACKGROUND: Ewing's sarcoma (ES) is a neuroectodermal tumor that typically occurs in the bones and soft tissues of children and young adults. Primary renal ES is rare; only a few cases and a small case series have been documented, and only four cases involved primary renal ES in older people (> 65 years old). CASE SUMMARY: Herein, we describe the radiological and pathological features of primary renal ES in an older person. A 76-year-old man complained of poor oral intake and was found to have a large cystic renal mass with indistinct margins on computed tomography. Ultrasound-guided biopsy revealed that the tumor contained small round blue cells. The patient underwent a right radical nephrectomy. The tumor cells showed diffuse membranous CD99, and nuclear friend leukemia integration 1 transcription factor and NK2 Homeobox 2. Fluorescence in situ hybridization revealed EWSR1 translocation. Postoperatively, 18F-fluorodeoxyglucose positron emission tomography revealed no evidence of metastasis. The patient was diagnosed with primary renal ES. Six months following the surgery, local recurrence and distant metastasis were observed. Primary renal ES is rare and often lethal in older individuals. The specific imaging findings are unknown, and treatment protocols have not been standardized. CONCLUSION: This case report describes the radiological and pathological features of primary renal ES in an older person.

Endobronchial Ultrasound Using Guide Sheath-Guided Transbronchial Lung Biopsy in Ground-Glass Opacity Pulmonary Lesions without Fluoroscopic Guidance.

Diagnosing ground-glass opacity (GGO) pulmonary lesions poses challenges. This study evaluates the utility of radial probe endobronchial ultrasound-guided transbronchial lung biopsy (RP-EBUS-TBLB) in diagnosing GGO pulmonary lesions. A total of 1651 RP-EBUS procedures were performed during the study period. This study analyzed 115 GGO lesions. The EBUS visualization yield was 80.1%. Of 115 lesions, 69 (60%) were successfully diagnosed. The average size of diagnosed lesions was significantly larger than that of undiagnosed lesions (21.9 ± 7.3 vs. 17.1 ± 6.6 mm, p < 0.001). Diagnostic yield varied by lesion size: 50.0% for lesions <20 mm, 65.1% for 20-30 mm lesions, and 85.7% for lesions >30 mm. The mixed blizzard sign on EBUS appeared in 60.6% of mixed GGO lesions, with no cases in pure GGO lesions. Multivariable analyses showed that lesion size (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.00-1.16; p < 0.001) and mixed blizzard sign on EBUS (OR, 20.92; CI, 7.50-58.31; p < 0.001) were significantly associated with diagnostic success. Pneumothorax and hemoptysis occurred in 1.7% and 2.6% of patients, respectively. RP-EBUS-TBLB without fluoroscopic guidance is a viable diagnostic approach for GGO pulmonary lesions with acceptable complications.

Slowly Growing Pulmonary Glandular Papilloma with Air Bronchogram: A Case Report.

Pulmonary glandular papilloma is a rare benign neoplasm that has not been studied extensively. This neoplasm presents as a solid nodule, consolidation, or mass, with or without atelectasis, and assessing the correlation between these findings and the risk of malignancy is challenging. A 60-year-old woman presented a solitary pulmonary nodule on screening chest radiography and chest computed tomography (CT). During the subsequent 2-year follow-up, CT showed a progressive increase in nodule size and an air bronchogram, suggesting malignancy. The patient underwent a right upper lobectomy, and the final diagnosis was glandular papilloma. Teaching point: Pulmonary glandular papilloma with growth and an air bronchogram.

Usual Interstitial Pneumonia: Associations With Complications After Percutaneous Transthoracic Needle Lung Biopsy.

BACKGROUND. Changes in lung parenchyma elasticity in usual interstitial pneumonia (UIP) may increase the risk for complications after percutaneous transthoracic needle biopsy (PTNB) of the lung. OBJECTIVE. The purpose of this article was to investigate the association of UIP findings on CT with complications after PTNB, including pneumothorax, pneumothorax requiring chest tube insertion, and hemoptysis. METHODS. This retrospective single-center study included 4187 patients (mean age, 63.8 ± 11.9 [SD] years; 2513 men, 1674 women) who underwent PTNB between January 2010 and December 2015. Patients were categorized into a UIP group and non-UIP group by review of preprocedural CT. In the UIP group, procedural CT images were reviewed to assess for traversal of UIP findings by needle. Multivariable logistic regression analyses were performed to identify associations between the UIP group and needle traversal with postbiopsy complications, controlling for a range of patient, lesion, and procedural characteristics. RESULTS. The UIP and non-UIP groups included 148 and 4039 patients, respectively; in the UIP group, traversal of UIP findings by needle was observed in 53 patients and not observed in 95 patients. The UIP group, in comparison with the non-UIP group, had a higher frequency of pneumothorax (35.1% vs 17.9%, p < .001) and pneumothorax requiring chest tube placement (6.1% vs 1.5%, p = .001) and lower frequency of hemoptysis (2.0% vs 6.1%, p = .03). In multivariable analyses, the UIP group with traversal of UIP findings by needle, relative to the non-UIP group, showed independent associations with pneumothorax (OR, 5.25; 95% CI, 2.94-9.37; p < .001) and pneumothorax requiring chest tube placement (OR, 9.55; 95% CI, 3.74-24.38; p < .001). The UIP group without traversal of UIP findings by needle, relative to the non-UIP group, was not independently associated with pneumothorax (OR, 1.18; 95% CI, 0.71-1.97; p = .51) or pneumothorax requiring chest tube placement (OR, 1.08; 95% CI, 0.25-4.72; p = .92). The UIP group, with or without traversal of UIP findings by needle, was not independently associated with hemoptysis. No patient experienced air embolism or procedure-related death. CONCLUSION. Needle traversal of UIP findings is a risk factor for pneumothorax and pneumothorax requiring chest tube placement after PTNB. CLINICAL IMPACT. When performing PTNB in patients with UIP, radiologists should plan a needle trajectory that does not traverse UIP findings, when possible.

75% radiation dose reduction using deep learning reconstruction on low-dose chest CT.

OBJECTIVE: Few studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR). MATERIALS AND METHODS: We retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC). RESULTS: The median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06). CONCLUSION: QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.

Nontuberculous Mycobacterial Infection Mimicking Lung Cancer in A Patient with Usual Interstitial Pneumonia Pattern Interstitial Lung Disease: A Case Report.

BACKGROUND: It has been reported that structure damage in the parenchymal lung disease such as idiopathic pulmonary fibrosis (IPF) is associated with high susceptibility to nontuberculous mycobacterial (NTM) infection. Radiologic features of NTM lung disease in destructive lung parenchyma can be atypical, which can cause confusion with other diseases including malignancy. Prompt and accurate identification of newly developed lesions in the follow-up computed tomography (CT) of IPF patients is challenging but crucial. CASE REPORT: We reported a case of an NTM infection in a patient with IPF, manifested as a mass-like consolidation with cavitation on chest CT, mimicking lung cancer. CONCLUSION: Being aware of the unusual radiologic features of NTM lung disease in IPF patients can be useful in the differential diagnosis of newly detected lesions.

Deep Learning for Detecting Pneumothorax on Chest Radiographs after Needle Biopsy: Clinical Implementation.

Background Accurate detection of pneumothorax on chest radiographs, the most common complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in practice. A computer-aided detection (CAD) system may help detect pneumothorax. Purpose To investigate whether a deep learning-based CAD system can improve detection performance for pneumothorax on chest radiographs after PTNB in clinical practice. Materials and Methods A CAD system for post-PTNB pneumothorax detection on chest radiographs was implemented in an institution in February 2020. This retrospective cohort study consecutively included chest radiographs interpreted with CAD assistance (CAD-applied group; February 2020 to November 2020) and those interpreted before implementation (non-CAD group; January 2018 to January 2020). The reference standard was defined by consensus reading by two radiologists. The diagnostic accuracy for pneumothorax was compared between the two groups using generalized estimating equations. Matching was performed according to whether the radiograph reader and PTNB operator were the same using the greedy method. Results A total of 676 radiographs from 655 patients (mean age: 67 years ± 11; 390 men) in the CAD-applied group and 676 radiographs from 664 patients (mean age: 66 years ± 12; 400 men) in the non-CAD group were included. The incidence of pneumothorax was 18.2% (123 of 676 radiographs) in the CAD-applied group and 22.5% (152 of 676 radiographs) in the non-CAD group (P = .05). The CAD-applied group showed higher sensitivity (85.4% vs 67.1%), negative predictive value (96.8% vs 91.3%), and accuracy (96.8% vs 92.3%) than the non-CAD group (all P < .001). The sensitivity for a small amount of pneumothorax improved in the CAD-applied group (pneumothorax of <10%: 74.5% vs 51.4%, P = .009; pneumothorax of 10%-15%: 92.7% vs 70.2%, P = .008). Among patients with pneumothorax, 34 of 655 (5.0%) in the non-CAD group and 16 of 664 (2.4%) in the CAD-applied group (P = .009) required subsequent drainage catheter insertion. Conclusion A deep learning-based computer-aided detection system improved the detection performance for pneumothorax on chest radiographs after lung biopsy. © RSNA, 2022 See also the editorial by Schiebler and Hartung in this issue.

Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.

OBJECTIVE: To explore the value of a deep learning-based algorithm in detecting Lung CT Screening Reporting and Data System category 4 nodules on chest radiographs from an asymptomatic health checkup population. METHODS: Data from an annual retrospective cohort of individuals who underwent chest radiographs for health checkup purposes and chest CT scanning within 3 months were collected. Among 3073 individuals, 118 with category 4 nodules on CT were selected. A reader performance test was performed using those 118 radiographs and randomly selected 51 individuals without any nodules. Four radiologists independently evaluated the radiographs without and with the results of the algorithm; and sensitivities/specificities were compared. The sample size needed to confirm the difference in detection rates was calculated, i.e., the number of true-positive radiographs divided by the total number of radiographs. RESULTS: The sensitivity of the radiologists substantially increased aided by the algorithm (38.8% [183/472] to 45.1% [213/472]; p < .001) without significant change in specificity (94.1% [192/204] vs. 92.2% [188/204]; p = .22). Pooled radiologists detected more nodules with the algorithm (32.0% [156/488] vs. 38.9% [190/488]; p < .001), without alteration of false-positive rates (0.09 [62/676], both). Pooled detection rates for the annual cohort were 1.49% (183/12,292) and 1.73% (213/12,292) without and with the algorithm, respectively. A sample size of 41,776 in each arm would be required to demonstrate significant detection rate difference with < 5% type I error and > 80% power. CONCLUSION: Although readers substantially increased sensitivity in detecting nodules on chest radiographs from a health checkup population aided by the algorithm, detection rate difference was only 0.24%, requiring a sample size >80,000 for a randomized controlled trial. KEY POINTS: • Aided by a deep learning algorithm, pooled radiologists improved their sensitivity in detecting Lung-RADS category 4 nodules on chest radiographs from a health checkup population (38.8% [183/472] to 45.1% [213/472]; p < .001), without increasing false-positive rate. • The prevalence of the Lung-RADS category 4 nodules was 3.8% (118/3073) on the population, resulting in only 0.24% increase of the detection rate for the radiologists with assistance of the algorithm. • To confirm the significant detection rate increase by a randomized controlled trial, a sample size of 84,000 would be required.

Prediction of visceral pleural invasion in lung cancer on CT: deep learning model achieves a radiologist-level performance with adaptive sensitivity and specificity to clinical needs.

OBJECTIVES: To develop and validate a preoperative CT-based deep learning model for the prediction of visceral pleural invasion (VPI) in early-stage lung cancer. METHODS: In this retrospective study, dataset 1 (for training, tuning, and internal validation) included 676 patients with clinical stage IA lung adenocarcinomas resected between 2009 and 2015. Dataset 2 (for temporal validation) included 141 patients with clinical stage I adenocarcinomas resected between 2017 and 2018. A CT-based deep learning model was developed for the prediction of VPI and validated in terms of discrimination and calibration. An observer performance study and a multivariable regression analysis were performed. RESULTS: The area under the receiver operating characteristic curve (AUC) of the model was 0.75 (95% CI, 0.67-0.84), which was comparable to those of board-certified radiologists (AUC, 0.73-0.79; all p > 0.05). The model had a higher standardized partial AUC for a specificity range of 90 to 100% than the radiologists (all p < 0.05). The high sensitivity cutoff (0.245) yielded a sensitivity of 93.8% and a specificity of 31.2%, and the high specificity cutoff (0.448) resulted in a sensitivity of 47.9% and a specificity of 86.0%. Two of the three radiologists provided highly sensitive (93.8% and 97.9%) but not specific (48.4% and 40.9%) diagnoses. The model showed good calibration (p > 0.05), and its output was an independent predictor for VPI (adjusted odds ratio, 1.07; 95% CI, 1.03-1.11; p < 0.001). CONCLUSIONS: The deep learning model demonstrated a radiologist-level performance. The model could achieve either highly sensitive or highly specific diagnoses depending on clinical needs. KEY POINTS: • The preoperative CT-based deep learning model demonstrated an expert-level diagnostic performance for the presence of visceral pleural invasion in early-stage lung cancer. • Radiologists had a tendency toward highly sensitive, but not specific diagnoses for the visceral pleural invasion.

Survivorship of implanted bone marrow-derived mesenchymal stem cells in acute rotator cuff tear.

BACKGROUND: This study examined whether a mesenchymal stem cells (MSCs)-seeded 3-dimensional construct into a tendon defect would promote cellular differentiation and matrix healing. MATERIALS AND METHODS: Bone marrow was harvested from the iliac crests of 2 male New Zealand White rabbits. The MSCs were cultured, and an open-cell polylactic acid (OPLA) scaffold was encapsulated with these cells. The injury model was a 5-mm × 5-mm-sized full-thickness window defect in the central part of each rotator cuff tendon. The defects on the right side were grafted with the autologous MSCs-seeded OPLA scaffold implant and a biodegradable suture. The same procedure was done on the left side, except a cell-free OPLA scaffold was used. Three rabbits were used as controls, without treatment of the tendon defect. Samples were harvested at 2, 4, and 6 weeks for analysis, which included evaluation of gross morphology, fluorescent analysis, histologic assessment, and immunohistochemistry studies. RESULTS: The expression of immunohistochemical stainings for collagen I was higher in the scaffold with MSCs than in the scaffold without MSCs. The expression of collagen II, however, was not different between the scaffolds with and without MSCs. CONCLUSIONS: Even though this is a short-term study, we demonstrated that many MSCs in the scaffold survived after implantation in an acute rabbit rotator cuff defect. Furthermore, the generation of type I collagen increased more in the scaffold with MSCs than it did in the scaffold without MSCs. MSCs are thought to promote tendon healing by producing type I collagen when they are applied at the tendon defect.

Predictors of survival after surgical treatment of spinal metastasis.

BACKGROUND: Surgery for spinal metastasis is a palliative treatment aimed at improving patient quality of life by alleviating pain and reversing or delaying neurologic dysfunction, but with a mean survival time of less than 1 year and significant complication rates, appropriate patient selection is crucial. OBJECTIVE: To identify the most significant prognostic variables of survival after surgery for spinal metastasis. METHODS: Chart review was performed on 200 surgically treated spinal metastasis patients at Stanford Hospital between 1999 and 2009. Survival analysis was performed and variables entered into a Cox proportional hazards model to determine their significance. RESULTS: Median overall survival was 8.0 months, with a 30-day mortality rate of 3.0% and a 30-day complication rate of 34.0%. A Cox proportional hazards model showed radiosensitivity of the tumor (hazard ratio: 2.557, P<.001), preoperative ambulatory status (hazard ratio: 2.355, P=.0001), and Charlson Comorbidity Index (hazard ratio: 2.955, P<.01) to be significant predictors of survival. Breast cancer had the best prognosis (median survival, 27.1 months), whereas gastrointestinal tumors had the worst (median survival, 2.66 months). CONCLUSION: We identified the Charlson Comorbidity Index score as one of the strongest predictors of survival after surgery for spinal metastasis. We confirmed previous findings that radiosensitivity of the tumor and ambulatory status are significant predictors of survival.

Requests for 692 transfers to an academic level I trauma center: implications of the emergency medical treatment and active labor act.

BACKGROUND: The Emergency Medical Treatment and Active Labor Act (EMTALA) effectively requires Level I trauma centers (TC) to accept all transfers for a higher level of care if capacity exists. We hypothesized that EMTALA would burden a Level I TC by a selective referral of a poor payer mix of primarily nonoperative patients. METHODS: All transfer calls (December 2003 and September 2005) to our Level I TC are handled by a dedicated transfer center. Calls were reviewed for age, surgical service requested, and outcome of request. The trauma registry was queried to compare Injury Severity Scale (ISS) score, hospital stay (LOS), operations, mortality, and payer status for transfer and primary catchment patients. RESULTS: In all, 821 calls were received; 77 calls were cancelled by the referring hospital and 52 were for consultation only. Of the 692 transfer requests, 534 (77%) were accepted, 134 (19%) were denied for no capacity, and only 24 (4%) were declined by TC as not clinically indicated. Transferred patients were younger (32.0 +/- 1.49 versus 38.9 +/- 0.51, p < 0.05), had similar ISS scores (13.6 +/- 0.62 versus 13.7 +/- 0.26) and LOS (7.0 +/- 0.70 versus 7.4 +/- 0.25), but were somewhat more likely to require an operation than direct admissions (58% versus 51%, p < 0.05). Although trauma (24%) and neurosurgery (24%) were the most commonly requested services, followed by orthopedics (20%), orthopedics accounted for 60% of operations on transferred patients compared with 10% to 13% for trauma and neurosurgery (mostly spine). There was no difference in the payer status of transfer and direct admit patients. CONCLUSIONS: Contrary to our assumptions, EMTALA patients had an identical payer mix and similar operative need compared with our primary catchment patients. They do represent a large additional patient load (20% of admissions) and differentially impact specialists, mostly operative for orthopedics and complex nonoperative care for trauma and neurosurgery. These data suggest that the primary motivations for transfer are specialist availability and complexity of care rather than financial concerns. As TCs provide backup specialty call coverage for a wide geographic area, this further supports the need for trauma systems development.

Thoracolumbar vertebral reconstruction after surgery for metastatic spinal tumors: long-term outcomes.

OBJECT: Metastatic spinal tumors continue to represent a major problem for patients and treating physicians. The purpose of this study was to assess quantitatively the functional outcome, quality of life, and survival rates of patients after major reconstructive spine surgery. METHODS: A prospective database was established and 58 patients were identified who had undergone thoracolumbar vertebral reconstruction for metastatic spinal tumors between March 1993 and October 1999. Surgical indications included disabling pain (92%) and/or progressive neurological dysfunction (60%). Forty-nine patients (85%) had clinical improvement in pain as determined based on the Oswestry pain scale (p < 0.05); 60% demonstrated improvement in their neurological status. The mean neurological improvement in Frankel grade was 1.2 (p < 0.05). The 12-month survival rate was 65%, and all patients who were ambulatory after surgery remained so until the time of death. Instrumentation failure requiring repeated operation occurred in two patients (3.5%), and in 12 patients (21%) local tumor recurrence necessitated repeated surgery. There were no cases of neurological deficit or death related to surgery. CONCLUSIONS: Major anterior thoracolumbar vertebral reconstruction is an effective treatment for local tumor control. More importantly, the authors have demonstrated that surgical treatment can significantly improve the quality of life by improvement of pain control and maintenance of ambulation during the patient's remaining life span.