Solid Nodules Research Articles

Prognostic Factors for Survival of Stage IB Non-small Cell Lung Cancer Patients: A 10-Year Follow-Up Retrospective Study.

This study aimed to determine the prognostic factors for the long-term outcome of stage IB non-small cell lung cancer (NSCLC). Surgically resected patients with stage IB NSCLC diagnosed (based on TNM 8th edition) between April 2008 and December 2013 were retrospectively reviewed. The prognosis and possible risk factors among the stage IB NSCLC patients were evaluated. Of the 349 patients identified for the study, 80 (22.9%) received post-surgery adjuvant chemotherapy (ACT). The median follow-up time after surgery was 123.3 months. The 10-year overall survival (OS) rate was 69.6%, and the 10-year recurrence-free survival (RFS) rate was 62.8%. The patients in this cohort were divided into three groups (T1 with visceral pleural invasion [VPI], T2a without VPI, and T2a with VPI), and no significant differences in OS or RFS were found among the groups. Furthermore, survival analysis indicated that the absence of ground-glass opacity (GGO) components portends an adverse long-term OS and RFS. In a subgroup of patients with solid nodules, age older than 65 years (hazard ratio [HR] 1.987; 95% confidence interval [CI] 1.312-3.010; p = 0.001) and ACT (HR 0.392; 95% CI 0.225-0.684; p < 0.001) were independent prognostic factors for OS, whereas lymphovascular invasion (HR 1.792; 95% CI 0.995-3.227; p = 0.052) should be considered as an independent unfavorable prognostic factor for RFS. As an upstaging factor, VPI did not further stratify prognosis for the stage IB patients in our cohort. The presence of GGO components had a notable impact on a favorable prognosis in stage IB NSCLCs.

18F-FDG PET/CT to Spare Malignancy in a Rare Case of Lung Endometriosis.

Lung endometriosis is a rare condition. A 44-year-old woman with known lung endometriosis and radiological follow-up showed progression of bilateral lung affection at control CT, with an enlarged solid nodule with respect to previous control. 18F-FDG PET/CT was performed to assess that lesion and to guide biopsy. PET images showed the multiple known nodules with pathological tracer uptake.

Differences of molecular events driving pathological and radiological progression of lung adenocarcinoma.

Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD. Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation. Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients' prognosis in 11 external LUAD datasets. Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD. This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas. Preoperative chest CT images for 215 patients with solid pulmonary nodules with histopathologically confirmed lung adenocarcinoma and cryptococcosis infection were collected from two clinical centers (108 cases in the training set and 107 cases in the test set divided by the different hospitals). Radiomics models were constructed based on nodular lesion volume (LV), 5-mm extended lesion volume (ELV), and perilesion volume (PLV). A demoradiological model was constructed using logistic regression based on demographic information (age, sex) and 12 radiological features (location, number, shape and specific imaging signs). Both models were used to build an integrated model, the performance of which was assessed using the test set. A junior and a senior radiologist evaluated the nodules. Receiver operating characteristic (ROC) curve analysis was conducted, and areas under the curve (AUCs), sensitivity (SEN), and specificity (SPE) of the models were calculated and compared. Among the radiomics models, AUCs of the LV, ELV, and PLV were 0.558, 0.757, and 0.470, respectively. Age, lesion number, and lobular sign were identified as independent discriminative features providing an AUC of 0.77 in the demoradiological model (SEN 0.815, SPE 0.642). The integrated model achieved the highest AUC of 0.801 (SEN 0.759, SPE 0.755), which was significantly higher than that obtained by a junior radiologist (AUC =0.689, P=0.024) but showed no significant difference from that of the senior radiologist (AUC =0.784, P=0.388). An integrated model with radiomics and demoradiological features improves discrimination of cryptococcosis granulomas from solid adenocarcinomas on noncontrast CT. This model may be an effective strategy for machine complementation to discrimination by radiologists, and whole-lung automated recognition methods might dominate in the future.

History of Thyroid Ultrasound.

From low-resolution images in the 1960s to current high-resolution technology, ultrasound has proven to be the initial imaging modality of choice for thyroid application. Point-of-care ultrasound has brought the technology to the thyroid specialist. Combined with physical examination, it provides real-time information regarding goiter, thyroid nodules, and thyroid cancer. Ultrasound-guided fine-needle aspiration biopsy has become the accepted norm, with biopsies rarely performed using palpation alone. Advantages of ultrasound-guided biopsy include precise placement of the needle within the nodule, selective sampling of areas with suspicious features, and accurate direction of the biopsy needle to actively growing viable cells in the periphery of the nodule. Education of endocrinologists in thyroid ultrasound began in the late 1990s and by 2016 more than 6000 clinicians had completed an ultrasound course. Concurrent with this rapid expansion of use of thyroid ultrasound was a rise in the diagnosis of small papillary carcinomas, which might have otherwise remained indolent and undetected. The 2009 American Thyroid Association Guidelines for the Management of Thyroid Nodules and Thyroid Cancer recommended biopsy for all solid hypoechoic nodules measuring larger than 1 cm. Attempting to decrease the frequency of biopsies of low-risk nodules, subsequent guidelines have focused on identifying and selectively biopsying those thyroid nodules at higher risk of clinically significant carcinoma based on ultrasound appearance. A major role for thyroid ultrasound has been in both preoperative staging and mapping to help determine the extent of surgery, as well as postoperative monitoring for locoregional soft tissue or lymph node metastases. With the recognition that the increase in papillary carcinoma was predominantly a result of early diagnosis of small often indolent cancers, active surveillance has become a promising management strategy for papillary thyroid microcarcinomas. Thyroid ultrasound is essential to active surveillance of thyroid cancer. Easy access to high-quality ultrasound studies is a requirement for a successful active surveillance program. Thyroid ultrasound has been used to facilitate interventional procedures, including treatment of thyroid nodules, treatment of recurrent thyroid cancer, and therapy of papillary thyroid microcarcinoma.

Radiological evidence of rapid growth acceleration of a small part solid nodule found to be large-cell carcinoma of the lung

BackgroundWith the widespread use of low-dose computed tomography for lung cancer screening, the detection rate of pulmonary lesions manifesting as ground-glass opacities (GGOs) has been increasing dramatically. The volume doubling time (VDT) has been introduced in clinical practice to monitor the potential growth rate of GGOs during long-term follow-up periods.Case presentationA 72-year-old never-smoker female diagnosed with mixed GGO manifested abruptly accelerated growth with sudden decreased VDT from 400 to 36 days. A thoracoscopic left lower lobectomy with mediastinal lymph node dissection was performed, and the diagnosis was stage IB large-cell neuroendocrine carcinoma (LCNEC). Next-generation sequencing of the tumor highlights an EML4-ALK gene fusion.ConclusionsThe LCNEC may present as GGO with longer VDT in the early stage. VDT should calculate by the whole size either on the entire tumor diameter or on consolidation diameter. It is recommended that meticulous long-term follow-up with dynamic VDT monitoring may help select high-risk GGOs performing timely semi-elective surgical resection in clinical practice.

Significance of marginal vessels in differentiating peripheral small-cell lung cancer and benign lung tumor.

Some peripheral small cell lung cancers (pSCLCs) and benign lung tumors (pBLTs) have similar morphological features but different treatment and prognosis. To determine the significance of marginal vessels in differentiating pSCLCs and pBLTs. A total of 57 and 95 patients with pathological confirmed nodular (≤3 cm) pSCLC and pBLT with similar morphological features were enrolled in this study retrospectively. The patients' clinical characteristics and computed tomography (CT) features of tumors and marginal vessels (vessels connecting with tumors) were analyzed and compared. Compared with pBLTs, pSCLCs had a larger diameter (P = 0.001) but lower enhancement (P = 0.015) and fewer had calcification (P = 0.013). Compared with pBLTs, more lesions had proximal (70.2% vs. 22.1%) and distal (59.6% vs. 4.2%) marginal vessels in pSCLCs (each P < 0.0001). In addition, in pSCLCs, the numbers of proximal (1.3 ± 1.4 vs. 0.3 ± 0.6), distal (2.4 ± 3.1 vs. 0.1 ± 0.5), and total (3.6 ± 3.5 vs. 0.4 ± 1.0) marginal vessels were all more than those in pBLTs (each P < 0.001). Receiver operating characteristic curve analysis revealed the positive distal marginal vessel sign had the highest specificity (95.8%), and the number of total marginal vessels had the best performance in discriminating pSCLC from pBLT (cutoff value = 1.5, AUC = 0.80, 95% CI = 0.72-0.89, sensitivity = 70.2%, and specificity = 91.6%). For peripheral solid nodules similar to pBLTs but without any calcification, the possibility of pSCLC should be considered if they have multiple marginal vessels (≥2), especially the distal ones.

Analysis of risk factors for intra-cystic hemorrhage in microwave ablation of partially cystic thyroid nodules.

The aim of this study is to identify risk factors of intra-cystic hemorrhage in microwave ablation of mixed solid and cystic microwave ablation s, and to design a preoperative nomogram to predict the risk value of intraoperative bleeding with the goal of individualizing the surgical approach toward different types of cystic and solid thyroid nodules. A total of 241 patients with cystic-solid thyroid nodules who underwent ultrasound-guided percutaneous microwave ablation were retrospectively divided into a bleeding group and a non-bleeding group to compare the diameter, cystic proportion, cystic fluid nature, color Doppler flow imaging, Contrast-enhanced ultrasound (CEUS) findings, and operative methods. Based on univariate and multivariate analysis, the important risk factors of nodular intracapsular hemorrhage in the ablation procedure were projected to a nomogram for predicting the possibility of intraoperative hemorrhage in the thyroid cystic solid nodules. Intra-cystic hemorrhage was developed in 37 cases during the ablation of mixed thyroid nodules with a total incidence of 15% (37/241). Significant differences were found statistically between the two groups on the diameter of the lesions, CEUS findings, the cystic fluid ratio, and operative methods (P = 0.000, P = 0.001, P = 0.024, P = 0.002). The possibility of intraoperative nodular intracapsular hemorrhage was predicted by the model based on the risk factors with the accuracy of 81% and prediction consistency index (C-index) of 0.78. A new and efficient prediction model was developed based on the identified risk factors for intracapsular hemorrhage during microwave ablation of mixed thyroid nodules, which will aid in the development of targeted surgical planning for different types of cystic thyroid nodules, thus reducing the risk of hemorrhage during ablation.

Different nodal upstaging rates and prognoses for patients with clinical T1N0M0 lung adenocarcinoma classified according to the presence of solid components in the lung and mediastinal windows.

Little is known about the correlation between nodal upstaging and pulmonary nodules classified according to the presence of solid components in the lung and mediastinal windows. This study thus aimed to analyze the risk factors of nodal upstaging and prognosis based on different imaging features, clinical characteristics, and pathological results from patients with clinical stage T1N0M0 lung adenocarcinoma. A total of 340 patients between January 2016 and June 2017 were selected from the Affiliated Hospital of Qingdao University database. Imaging features, clinical characteristics, and pathological results were collected for survival and analysis of nodal upstaging risk factors. We used logistic regression models to identify important metastatic risk factors for nodal upstaging. Survival rates were calculated using Kaplan-Meier (KM) survival curves and compared with the log-rank test. Significant prognostic risk factors were identified using the Cox proportional hazards model. A total of 340 patients, with an average age of 64.89 (±8.775) years, were enrolled. Among them, nonnodal upstaging occurred both in 77 (22.6%) patients with pure ground-glass nodules (pGGNs) and in 30 (8.8%) patients with heterogenous ground-glass nodules (hGGNs). Compared to the 92 (27.1%) patients with real part-solid nodules (rPSNs), the 141 (41.5%) patients with solid nodules were significantly different in terms of in nodal upstaging (P<0.001). Moreover, preoperative carcinoembryonic antigen (CEA) level >3.4 µg/L [odds ratio (OR): 2.931; 95% confidence interval (CI): 1.511-5.688; P=0.001], imaging tumor size >18.3 mm (OR, 3.482; 95% CI: 1.609-7.535; P=0.002), and consolidation tumor ratio (CTR) >0.788 (OR 8.791; 95% CI: 3.570-21.651; P<0.001) were independent risk factors for nodal upstaging. The KM survival curve results showed that patients with pGGNs and those with hGGNs had a much better 5-year disease-free survival (DFS) and 5-year overall survival (OS) than did those with rPSNs and those with solid nodules (DFS: 98.7% vs. 100% vs. 81.4% vs. 73.7%, P<0.001; OS: 97.4% vs. 100% vs. 90.2% vs. 83.7%, P=0.003). In the multivariate Cox regression analysis of patients with rPSNs and solid nodules, tumor location and pathological lymph node grade were found to be independent risk factors for DFS and OS. Patients with pGGNs and those with hGGNs were more likely to be free of nodal upstaging and had better prognosis than did those with clinical stage IA rPSNs and solid nodules. The patients with pGGNs or hGGNs with preoperative CEA level <3.4 µg/L, imaging tumor size <18.3 mm, and CTR <0.788 can choose systematic lymph node sampling (SLNS) or decline lymph node dissection to avoid postoperative complications.

Computed tomography (CT) characteristics and pathologic basis of ciliated muconodular papillary tumors of the lung.

Ciliated muconodular papillary tumor (CMPT) is a rare pulmonary tumor with papillary architecture. Most studies have focused on the clinicopathological features of CMPT, while computed tomography (CT) characteristics have rarely been systematically described. A cohort of 27 patients with surgically resected CMPT were identified. Clinical and demographic features were recorded. Preoperative CT images of the CMPTs and the corresponding histopathological basis were also retrospectively analyzed. All of the tumors appeared as solitary nodules. Pure ground glass, part-solid nodules and solid nodules were detected in 2/27 (7.4%), 17/27 (63.0%), and 8/27 (29.6%) patients, respectively. Twenty-one tumors (77.8%) were located in the lower lobe. The average tumor size was 1.21±0.74 (range, 0.44-3.46) cm. Eighteen (66.7%) of the 27 patients had tumors with well-defined margins and lobulated contours. Fifteen patients (55.6%) had air bronchograms in the tumor, and 19 patients (70.4%) had air-containing space. There were two patients whose tumor size was enlarged and accompanied by an increase in solid components, and one patient simply had an increase in tumor size at the preoperative follow-up duration. Notably, one patient with solid tumor components was finally diagnosed with CMPT accompanied by adenocarcinoma. CMPTs of the lung mostly manifest as solitary, lobulated, well-defined tumors with air-containing spaces on CT and often occur in the periphery of the pulmonary lower lobe. When CT findings meet these criteria, the possibility of CMPT should be considered. Additionally, CMPT can coexist with adenocarcinoma. Further investigation will contribute significantly to the biological properties of CMPT and its relationship to the potential for malignant transformation.

CLSSL-ResNet: Predicting malignancy of solitary pulmonary nodules from CT images by chimeric label with self-supervised learning.

Pulmonary granulomatous nodules (GN) with spiculation or lobulation have a similar morphological appearance to solid lung adenocarcinoma (SADC) under computed tomography (CT). However, these two kinds of solid pulmonary nodules (SPN) have different malignancies and are sometimes misdiagnosed. This study aims to predict malignancies of SPNs by a deep learning model automatically. A chimeric label with self-supervised learning (CLSSL) is proposed to pre-train a ResNet-based network (CLSSL-ResNet) for distinguishing isolated atypical GN from SADC in CT images. The malignancy, rotation, and morphology labels are integrated into a chimeric label and utilized to pre-train a ResNet50. The pre-trained ResNet50 are then transferred and fine-tuned to predict the malignancy of SPN. Two image datasets of 428 subjects (Dataset1, 307; Dataset2, 121) from different hospitals are collected. Dataset1 is divided into training, validation, and test data by a ratio of 7:1:2 to develop the model. Dataset2 is utilized as an external validation dataset. CLSSL-ResNet achieves an area under the ROC curve (AUC) of 0.944 and an accuracy (ACC) of 91.3%, which was much higher than that of the consensus of two experienced chest radiologists (77.3%). CLSSL-ResNet also outperforms other self-supervised learning models and many counterparts of other backbone networks. In Dataset2, AUC and ACC of CLSSL-ResNet are 0.923 and 89.3%, respectively. Additionally, the ablation experiment result indicates higher efficiency of the chimeric label. CLSSL with morphology labels can increase the ability of feature representation by deep networks. As a non-invasive method, CLSSL-ResNet can distinguish GN from SADC via CT images and may support clinical diagnoses after further validation.

Proposal of Modified Lung-RADS in Assessing Pulmonary Nodules of Patients with Previous Malignancies: A Primary Study

Background: In addition to the diameters of pulmonary nodules, the number and morphology of blood vessels in pure ground-glass nodules (pGGNs) were closely related to the occurrence of lung cancer. Moreover, the benign and malignant signs of nodules were also valuable for the identification of nodules. Based on these two points, we tried to revise Lung-RADS 2022 and proposed our Modified Lung-RADS. The aim of the study was to verify the diagnostic performance of Modified Lung-RADS for pulmonary solid nodules (SNs) and pure ground-glass nodules (pGGNs) in patients with previous malignancies. Methods: The chest CT and clinical data of patients with prior cancer who underwent pulmonary nodulectomies from 1 January 2018 to 30 November 2021 were enrolled according to inclusion and exclusion criteria. A total of 240 patients with 293 pulmonary nodules were included in this study. In contrast with the original version, the risk classification of pGGNs based on the GGN–vascular relationships (GVRs), and the SNs without burrs and with benign signs, could be downgraded to category 2. The sensitivity, specificity, and agreement rate of the original Lung-RADS 2022 and Modified Lung-RADS for pGGNs and SNs were calculated and compared. Results: Compared with the original version, the sensitivity and agreement rate of the Modified version for pGGNs increased from 0 and 23.33% to 97.10% and 92.22%, respectively, while the specificity decreased from 100% to 76.19%. As regards SNs, the specificity and agreement rate of the Modified version increased from 44.44% to 75.00% (p &lt; 0.05) and 88.67% to 94.09% (p = 0.052), respectively, while the sensitivity was unchanged (98.20%). Conclusions: In general, the diagnostic efficiency of Modified Lung-RADS was superior to that of the original version, and Modified Lung-RADS could be a preliminary attempt to improve Lung-RADS 2022.

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images

This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma.

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

Correlation exploration among CT imaging, pathology and genotype of pulmonary ground-glass opacity.

To analyse the clinical features, imaging manifestation, pathological typing and genetic testing results of patients undergoing surgery for ground-glass opacity (GGO) nodules, and explore the reasonable diagnosis and treatment program for GGO patients as to provide the basis for the establishment of GGO treatment process. This study is an exploratory study. 465 cases with GGO confirmed by HRCT, undergoing surgery and approved by pathologic diagnosis in Shanghai pulmonary hospital were enrolled in this study. All the patients with GGO were cases with single lesion. The relationship between the clinical, imaging, pathological and molecular biological data of single GGO were statistically studied. (1) Among 465 cases, the median age was 58 years and females were 315 (67.7%); there were 397 (85.4%) non-smoking, and 354 cases (76.1%) had no clinical symptoms. There were 33 cases of benign and 432 cases of malignant GGO. Significant differences were observed on the size, vacuole sign, pleural indentation and blood vessel sign of GGO between two groups (p < 0.05). Of 230 mGGO, there were no AAH, 13 cases of AIS, 25 cases of MIA and 173 cases of invasive adenocarcinoma. The probability of solid nodules in invasive adenocarcinoma was higher than that in micro invasive carcinoma, and the difference was statistically significant (p < 0.05). 360 cases were followed up with the average follow-up time of 6.05 months, and GGO of 34 cases (9.4%) increased. (2) In 428 adenocarcinoma samples approved by pathologic diagnosis, there were 262 (61.2%) lesions of EGFR mutation, 14 (3.3%) lesions of KRAS mutation, 1 (0.2%) lesion of Braf mutation, 9 (2.1%) lesions of EML4-ALK gene fusion and 2 (0.5%) lesions of ROS1 fusion. The detection rate of gene mutation in mGGO was higher than that of pGGO. During the follow-up period, genetic testing results of 32 GGO showed that EGFR mutation rate was 53.1%, ALK positive rate of 6.3%, KRAS mutation rate of 3.1% and no ros1 and BRAF gene mutation. No statistically significant difference was observed in comparison with unchanged GGO. (3) EGFR mutation rate of invasive adenocarcinoma was the highest (168/228, 73.7%), mainly in the 19Del and L858R point mutations. No KRAS mutation was found in atypical adenoma hyperplasia. No significant difference was observed on the mutation rate of KRAS between different types of GGO (p = 0.811). EML4-ALK fusion gene was mainly detected in invasive adenocarcinoma (7/9). GGO tends to occur in young, non-smoking women. The size of GGO is related to the degree of malignancy. Pleural depression sign, vacuole sign and vascular cluster sign are all characteristic images of malignant GGO. pGGO and mGGO reflect the pathological development of GGO. During the follow-up, it is found that GGO increases and solid components appear, which is the indication of surgical resection. The detection rate of EGFR mutations in mGGO and invasive adenocarcinoma is high. pGGO has heterogeneity in imaging, pathology and molecular biology. Heterogeneity research helps to formulate correct individualized diagnosis and treatment plans.

ANALYSIS OF CLINICAL, RADIOGRAPHIC, AND HISTOLOGICAL CHARACTERISTICS OF AN ADENOMATOID ODONTOGENIC TUMOR

Objective This study aimed to evaluate the clinical, radiographic, and histopathological characteristics of 20 cases of AOT. Study Design Clinical and radiographic data were collected from the patients’ files, recorded over a 17-year period. Age, sex, race, location, size, duration of the lesion, AOT variant, radiographic aspects of density, locularity and edges, and detailed histopathological features were analyzed. Results Sixteen patients (80.0%) were female and 4 (20.0%) were male, with a mean age of 22.7 years. All lesions were intraosseous and affected the maxilla (70.0%) and mandible (30.0%), with a mean size of 39.4 mm and a mean injury duration of 14.8 months. Twelve tumors (85.7%) were follicular and 3 (14.3%) were extrafollicular. Five patients presented pain (31.3%). Microscopically, 12 cases showed a predominantly solid pattern (60.0%), three predominantly cystic (15.0%), and five (25.0%) without predominance. Classic histological patterns were identified in the following proportion in the cases: solid nodules (95.0%), lace-like cords (90.0%), rosettes (90.0%), duct-like structures (90.0%) and areas of mineralization (80.0%). Conclusion Clinical, radiographic, and histopathological characteristics in this case series from Brazil are similar to findings in other populations. This study aimed to evaluate the clinical, radiographic, and histopathological characteristics of 20 cases of AOT. Clinical and radiographic data were collected from the patients’ files, recorded over a 17-year period. Age, sex, race, location, size, duration of the lesion, AOT variant, radiographic aspects of density, locularity and edges, and detailed histopathological features were analyzed. Sixteen patients (80.0%) were female and 4 (20.0%) were male, with a mean age of 22.7 years. All lesions were intraosseous and affected the maxilla (70.0%) and mandible (30.0%), with a mean size of 39.4 mm and a mean injury duration of 14.8 months. Twelve tumors (85.7%) were follicular and 3 (14.3%) were extrafollicular. Five patients presented pain (31.3%). Microscopically, 12 cases showed a predominantly solid pattern (60.0%), three predominantly cystic (15.0%), and five (25.0%) without predominance. Classic histological patterns were identified in the following proportion in the cases: solid nodules (95.0%), lace-like cords (90.0%), rosettes (90.0%), duct-like structures (90.0%) and areas of mineralization (80.0%). Clinical, radiographic, and histopathological characteristics in this case series from Brazil are similar to findings in other populations.

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest

PurposeTo test the performance of a commercially available adult pulmonary nodule detection artificial intelligence (AI) tool in pediatric CT chests. Methods30 consecutive chest CTs with or without contrast of patients ages 12–18 were included. Images were retrospectively reconstructed at 3 mm and 1 mm slice thickness. AI for detection of lung nodules in adults (Syngo CT Lung Computer Aided Detection (CAD)) was evaluated. 3 mm axial images were retrospectively reviewed by two pediatric radiologists (reference read) who determined the location, type, and size of nodules. Lung CAD results at 3 mm and 1 mm slice thickness were compared to reference read by two other pediatric radiologists. Sensitivity (Sn) and positive predictive value (PPV) were analyzed. ResultsThe radiologists identified 109 nodules. At 1 mm, CAD detected 70 nodules; 43 true positive (Sn = 39 %), 26 false positive (PPV = 62 %), and 1 nodule which had not been identified by radiologists. At 3 mm, CAD detected 60 nodules; 28 true positive (Sn = 26 %), 30 false positive (PPV = 48 %) and 2 nodules which had not been identified by radiologists. There were 103 solid nodules (47 measuring < 3 mm) and 6 subsolid nodules (5 measuring < 5 mm). When excluding 52 nodules (solid < 3 mm and subsolid < 5 mm) based on algorithm conditions, the Sn increased to 68 % at 1 mm and 49 % at 3 mm but there was no significant change in the PPV measuring 60 % at 1 mm and 48 % at 3 mm. ConclusionThe adult Lung CAD showed low sensitivity in pediatric patients, but better performance at thinner slice thickness and when smaller nodules were excluded.

Descripción de 4 casos: tumor desmoide de la mama, diagnóstico y manejo de un tumor infrecuente

IntroductionThe desmoid tumor (DT) of the breast is a rare lesion consisting of proliferation of fibroblastic cells. It can be located intra- or extra-abdominally and the mammary location is exceptional. Although it is a benign lesion, it has a locally aggressive behavior without lymph node or distant metastasis. Its etiology is unknown and 10–20% of cases are related to familial adenomatous polyposis. It presents as a new, painless solid nodule. The treatment has traditionally been surgical resection, with high rates of recurrence despite free margins. Other therapies are being studied. Our objective is to review the management of DT of the breast through the analysis of the casuistry in our center in the last 10 years and the review of the available evidence. MethodsRetrospective descriptive analysis. The cases were identified using the hospital information exploitation system. A narrative review of the literature was performed. ResultsOur series consists of 4 cases, women between 36 and 66 years old. Three of them consulted for a palpable nodule. The CNB presented findings compatible with DT. The cases were presented to a multidisciplinary committee and a wide surgical resection was proposed. The AP confirmed the diagnosis and in one of the cases reported affected margins. During follow-up, recurrence was observed in this case. ConclusionsDTs are infrequent tumors, with clinical repercussions and variable response to treatment. This must include the expectant option and, in any case, be individualized and agreed upon within a multidisciplinary perspective.

An integrated convolutional neural network for classifying small pulmonary solid nodules.

Achieving accurate classification of benign and malignant pulmonary nodules is essential for treating some diseases. However, traditional typing methods have difficulty obtaining satisfactory results on small pulmonary solid nodules, mainly caused by two aspects: (1) noise interference from other tissue information; (2) missing features of small nodules caused by downsampling in traditional convolutional neural networks. To solve these problems, this paper proposes a new typing method to improve the diagnosis rate of small pulmonary solid nodules in CT images. Specifically, first, we introduce the Otsu thresholding algorithm to preprocess the data and filter the interference information. Then, to acquire more small nodule features, we add parallel radiomics to the 3D convolutional neural network. Radiomics can extract a large number of quantitative features from medical images. Finally, the classifier generated more accurate results by the visual and radiomic features. In the experiments, we tested the proposed method on multiple data sets, and the proposed method outperformed other methods in the small pulmonary solid nodule classification task. In addition, various groups of ablation experiments demonstrated that the Otsu thresholding algorithm and radiomics are helpful for the judgment of small nodules and proved that the Otsu thresholding algorithm is more flexible than the manual thresholding algorithm.

Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules.

To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs). A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (p = 0.03), cystic fluid transmission (p = 0.02), longitudinal diameter (p < 0.001), and age (p = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (M is the malignancy score, e = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model. A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.