The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.

OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Value of ultrathin bronchoscope in improving the endobronchial ultrasound localization rate and diagnosing peripheral pulmonary nodules by cryobiopsy.

BACKGROUND: A 3.0-mm ultrathin bronchoscope (UTB) with a 1.7-mm working channel provides better accessibility to peripheral bronchi. A 4.0-mm thin bronchoscope with a larger 2.0-mm working channel facilitates the use of a guide sheath (GS), ensuring repeated sampling from the same location. The 1.1-mm ultrathin cryoprobe has a smaller diameter, overcoming the limitation of the size of biopsy instruments used with UTB. In this study, we compared the endobronchial ultrasound localization rate and diagnostic yield of peripheral lung lesions by cryobiopsy using UTB and thin bronchoscopy combined with GS. METHODS: We retrospectively evaluated 133 patients with peripheral pulmonary lesions with a diameter less than 30 mm who underwent bronchoscopy with either thin bronchoscope or UTB from May 2019 to May 2023. A 3.0-mm UTB combined with rEBUS was used in the UTB group, whereas a 4.0-mm thin bronchoscope combined with rEBUS and GS was used for the thin bronchoscope group. A 1.1-mm ultrathin cryoprobe was used for cryobiopsy in the two groups. RESULTS: Among the 133 patients, peripheral pulmonary nodules in 85 subjects were visualized using r-EBUS. The ultrasound localization rate was significantly higher in the UTB group than in the thin bronchoscope group (96.0% vs. 44.6%, respectively; P < 0.001). The diagnostic yield of cryobiopsy specimens from the UTB group was significantly higher compared to the thin bronchoscope group (54.0% vs. 30.1%, respectively; p = 0.006). Univariate analysis demonstrated that the cryobiopsy diagnostic yields of the UTB group were significantly higher for lesions ≤ 20 mm, benign lesions, upper lobe lesions, lesions located lateral one-third from the hilum, and lesions without bronchus sign. CONCLUSIONS: Ultrathin bronchoscopy combined with cryobiopsy has a superior ultrasound localization rate and diagnostic yield compared to a combination of cryobiopsy and thin bronchoscopy.

Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.

OBJECTIVE: To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning. METHODS: pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models. RESULTS: The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet_3D, 3D_3F, and 3D_19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models. CONCLUSIONS: The dual-head ResNet_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.

[Common issues and solutions in intraoperative frozen pathological diagnosis of small pulmonary nodules].

With the development of chest CT screening, surgically resected lung tumors have shifted from predominantly large masses to predominantly small nodules. The intraoperative frozen diagnosis of pulmonary small nodules faces many challenges, such as the accurate understanding about the concepts of adenocarcinoma in situ, minimally invasive adenocarcinoma and lepidic adenocarcinoma, as well as their differential diagnosis with small size invasive adenocarcinoma, benign tumors (such as bronchiolar adenoma, sclerosing pneumocytoma, etc.), metastatic tumors and so on. This study summarizes some common problems encountered in the intraoperative frozen diagnosis of small pulmonary nodules in daily practice, focusing on the diagnosis and differential diagnosis of adenocarcinoma, in order to make the accurate intraoperative frozen diagnosis of small pulmonary nodules and diminish misdiagnosis.

Shape-sensing Robotic-assisted Bronchoscopy (SS-RAB) in Sampling Peripheral Pulmonary Nodules: A Prospective, Multicenter Clinical Feasibility Study in China.

BACKGROUND: The ION system is a shape-sensing robotic-assisted bronchoscopy (SS-RAB) platform developed to biopsy peripheral pulmonary nodules (PPNs). There is a lack of data describing the use of this system in the Chinese population. The study aimed to assess the feasibility and safety of using SS-RAB to diagnose PPNs across multiple centers within China. METHODS: This prospective, multicenter study used SS-RAB in consecutive patients with solid or sub-solid PPNs 8 to 30 mm in largest diameter. Primary endpoints were diagnostic yield and the rates of procedure- or device-related complications. Radial endobronchial ultrasound (rEBUS) was to confirm lesion localization, followed by sampling, using the Flexision biopsy needle, biopsy forceps, and cytology brush. Subjects with nonmalignant index biopsy results were followed up to 6 months. RESULTS: A total of 90 PPNs were biopsied from 90 subjects across 3 centers using SS-RAB. The median nodule size was 19.4 mm (IQR: 19.3, 24.6) in the largest dimension. In all (100%) cases, the catheter successfully reached the target nodule with tissue samples obtained. The diagnostic yield was 87.8% with a sensitivity for malignancy of 87.7% (71/81). In a univariate analysis, nodule lobar location, presence of bronchus sign, and rEBUS view were associated with a diagnostic sample, but only rEBUS view showed an association in a multivariate analysis. The overall pneumothorax rate was 1.1% without pneumothorax requiring intervention, and there was no periprocedural bleeding. CONCLUSION: As an emerging technology in the Chinese population, SS-RAB can safely biopsy PPNs with strong diagnostic performance.

Distribution of Solid Lung Nodules Presence and Size by Age and Sex in a Northern European Nonsmoking Population.

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm3), clinically relevant lung nodules (≥100 mm3), and actionable nodules (≥300 mm3) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [n = 5908] female participants; 46.1% [n = 4812] never smokers and 53.9% [n = 5619] former smokers) were included. Of these, 42.0% (n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

A 62-Year-Old Woman With Cough, Dyspnea, and Diffuse Lung Nodules.

CASE PRESENTATION: A 62-year-old woman came to our hospital with worsening cough and dyspnea over the preceding week, during which time she had been treated with azithromycin and prednisone for suspected pneumonia. She had no fever, chills, or sweats, but her cough had become productive of clear to blood-tinged phlegm during the interval. Medical history was significant for insulin-dependent diabetes mellitus and OSA. She had quit smoking 44 years earlier and had no history of lung disease. She was a bank teller residing in southeastern Minnesota and described no relevant inhalational or environmental exposures, drug use, aspiration, or travels preceding her illness.

Growth dynamics of lung nodules: implications for classification in lung cancer screening.

BACKGROUND: Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant. METHODS: Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods. RESULTS: Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year). CONCLUSIONS: In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

Expert consensus on the multidisciplinary diagnosis and treatment of multiple ground glass nodule-like lung cancer (2024 Edition).

ABSTRACT: This expert consensus reviews current literature and provides clinical practice guidelines for the diagnosis and treatment of multiple ground glass nodule-like lung cancer. The main contents of this review include the following: ① follow-up strategies, ② differential diagnosis, ③ diagnosis and staging, ④ treatment methods, and ⑤ post-treatment follow-up.

Prevalence of pulmonary nodules detected incidentally on noncancer-related imaging: a review.

Pulmonary nodules are common incidental findings requiring surveillance. Follow-up recommendations vary depending on risk factors, size and solid or subsolid characteristics. This review aimed to evaluate the prevalence of clinically significant nodules detected on noncancer-dedicated imaging and the prevalence of part-solid and ground-glass nodules. We conducted a systematic search of literature and screened texts for eligibility. Clinically significant nodules were noncalcified nodules >4-6 mm. Prevalence estimates were calculated for all studies and risk of bias was assessed by one reviewer. Twenty-four studies were included, with a total of 30 887 participants, and 21 studies were cross-sectional in design. Twenty-two studies used computed tomography (CT) imaging with cardiac-related CT being the most frequent. Prevalence of significant nodules was highest in studies with large field of view of the chest and low size thresholds for reporting nodules. The prevalence of part-solid and ground-glass nodules was only described in two cardiac-related CT studies. The overall risk of bias was low in seven studies and moderate in 17 studies. While current literature frequently reports incidental nodules on cardiovascular-related CT, there is minimal reporting of subsolid characteristics. Unclear quantification of smoking history and heterogeneity of imaging protocol also limits reliable evaluation of nodule prevalence in nonscreening cohorts.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study.

PURPOSE: We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC). METHODS: A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively. CONCLUSIONS: The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay.

BACKGROUND: Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling. METHODS: An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model. RESULTS: Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group. CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students.

BACKGROUND: To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students. METHODS: The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. RESULTS: There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. CONCLUSION: The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Analysis of complication risk factors in preoperative computed tomography-guided hookwire location of pulmonary nodules.

BACKGROUND: This study aimed to explore the efficacy of hookwire for computed tomography (CT)-guided pulmonary nodule (PN) localization before video-assisted thoracoscopic surgery (VATS) resection and determine the risk factors for localization-related complications. METHODS: We enrolled 193 patients who underwent preoperative CT-guided PN hookwire localization. The patients were categorized into groups A (103 patients had no complications) and B (90 patients had complications) according to CT and VATS. Uni- and multivariate logistic regression analyses were used to identify risk factors for localization-related complications. A numerical rating scale was used to evaluate hookwire localization-induced pain. RESULTS: We successfully performed localization in 173 (89.6%) patients. Pneumothorax was the main complication in 82 patients (42.5%). Patient gender, age, body mass index, tumor diameter, consolidation tumor ratio, pathologic diagnosis, position adjustment during location, lesion location, waiting time for surgery, and pleural adhesions were not significantly different between the two groups. The number of nodules, number of punctures, scapular rest position, and depth of insertion within the lung parenchyma were significant factors for successful localization. Multivariate regression analysis further validated the number of nodules, scapular rest position, and depth of insertion within the lung parenchyma as risk factors for hookwire-localization-related complications. Hookwire localization-induced pain is mainly mild or moderate pre- and postoperatively, and some patients still experience pain 7 days postoperatively. CONCLUSIONS: Hookwire preoperative PN localization has a high success rate, but some complications remain. Thus, clinicians should be vigilant and look forward to further improvement.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images.

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Diagnostic accuracy of low-dose dual-input computed tomography perfusion in the differential diagnosis of pulmonary benign and malignant ground-glass nodules.

This study aimed to evaluate the value of low-dose dual-input computed tomography perfusion (CTP) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGO). A retrospective study was conducted in patients with GGO who underwent CTP in our hospital from January 2021 to October 2023. All nodules were confirmed via pathological analysis or disappeared during follow-up. Postprocessing analysis was conducted using the dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CTP (348 mGy.cm) was < 75% of the diagnostic reference level of the unenhanced chest CT (470 mGy.cm). The effective radiation dose was 4.872 mSV. The blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) of malignant nodules were higher than those of the benign nodules (p < 0.05). The FEP had the highest accuracy for the diagnosis of malignant nodules (area under the curve [AUC] = 0.821, 95% confidence interval [CI]: 0.735-0.908) followed by BV (AUV = 0.713, 95% CI 0.608-0.819), BF (AUC = 0.688, 95% CI 0.587-0.797), and MTT (AUC = 0.616, 95% CI 0.506-0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. To distinguish between benign nodules and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI 0.728-0.903), whereas the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI 0.729-0.900). Low-dose dual-input perfusion CT was extremely effective in distinguishing between benign from malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability.

[Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules].

Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates for early-stage lung cancers (stages IA1, IA2, IA3, and IB, according to the TNM staging system) are significantly higher, underscoring the critical importance of early detection, diagnosis, and treatment. Ground-glass nodules (GGNs), which are commonly seen on lung imaging, can be indicative of both benign and malignant lesions. For clinicians, accurately characterizing GGNs and choosing the right management strategies present significant challenges. Artificial intelligence (AI), specifically deep learning algorithms, has shown promise in the evaluation of GGNs by analyzing complex imaging data and predicting the nature of GGNs, including their benign or malignant status, pathological subtypes, and genetic mutations such as epidermal growth factor receptor (EGFR) mutations. By integrating imaging features and clinical data, AI models have demonstrated high accuracy in distinguishing between benign and malignant GGNs and in predicting specific pathological subtypes. In addition, AI has shown promise in predicting genetic mutations such as EGFR mutations, which are critical for personalized treatment decisions in lung cancer. While AI offers significant potential to improve the accuracy and efficiency of GGN assessment, challenges remain, such as the need for extensive validation studies, standardization of imaging protocols, and improving the interpretability of AI algorithms. In summary, AI has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions. However, further research and validation are needed to fully realize the benefits of AI in clinical practice.

The correlation between KRAS and TP53 gene mutations and early growth of pulmonary nodules.

PURPOSE: The purpose of this study is to investigate whether gene mutations can lead to the growth of malignant pulmonary nodules. METHODS: Retrospective analysis was conducted on patients with pulmonary nodules at Hebei Provincial People's Hospital, collecting basic clinical information such as gender, age, BMI, and hematological indicators. According to the inclusion and exclusion criteria, 85 patients with malignant pulmonary nodules were selected for screening, and gene mutation testing was performed on all patient tissues to explore the relationship between gene mutations and the growth of malignant pulmonary nodules. RESULTS: There is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules (P < 0.05), while there is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules in the subgroup of invasive malignant pulmonary nodules (P < 0.05). CONCLUSION: Mutations in the TP53 gene can lead to the growth of malignant pulmonary nodules and are correlated with the degree of invasion of malignant pulmonary nodules.

Optimal approach for diagnosing peripheral lung nodules by combining electromagnetic navigation bronchoscopy and radial probe endobronchial ultrasound.

INTRODUCTION: Electromagnetic navigation bronchoscopy (ENB) and radial probe endobronchial ultrasound (RP-EBUS) are essential bronchoscopic procedures for diagnosing peripheral lung lesions. Despite their individual advantages, the optimal circumstances for their combination remain uncertain. METHODS: This single-center retrospective study enrolled 473 patients with 529 pulmonary nodules who underwent ENB and/or RP-EBUS biopsies between December 2021 and December 2022. Diagnostic yield was calculated using strict, intermediate, and liberal definitions. In the strict definition, only malignant and specific benign lesions were deemed diagnostic at the time of the index procedure. The intermediate and liberal definitions included additional results from the follow-up period. RESULTS: The diagnostic yield of the strict definition was not statistically different among the three groups (ENB/Combination/RP-EBUS 63.8%/64.2%/62.6%, p = 0.944). However, the diagnostic yield was superior in the ENB + RP-EBUS group for nodules with a bronchus type II or III and a solid part <20 mm (odds ratio 1.96, 95% confidence interval 1.09-3.53, p = 0.02). In terms of complications, bleeding was significantly higher in the ENB + RP-EBUS group (ENB/Combination/RP-EBUS 3.7% /6.2/0.6%, p = 0.002), but no major adverse event was observed. CONCLUSION: The combination of ENB and RP-EBUS enhanced the diagnostic yield for nodules with bronchus type II or III and solid part <20 mm, despite a slightly elevated risk of bleeding. Careful patient selection based on nodule characteristics is important to benefit from this combined approach.

[A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning].

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.