Pulmonary nodules as incidental findings

Radiomics and Clinical Data for the Diagnosis of Incidental Pulmonary Nodules and Lung Cancer Screening: Radiolung Integrative Predictive Model

INTRODUCTION

Early diagnosis of lung cancer (LC) is crucial to improve survival rates. Radiomics models hold promise for enhancing LC diagnosis. This study assesses the impact of integrating a clinical and a radiomic model based on deep learning to predict the malignancy of pulmonary nodules (PN).

METHODOLOGY

Prospective cross-sectional study of 97 PNs from 93 patients. Clinical data included epidemiological risk factors and pulmonary function tests. The region of interest of each chest CT containing the PN was analysed. The radiomic model employed a pre-trained convolutional network to extract visual features. From these features, 500 with a positive standard deviation were chosen as inputs for an optimised neural network. The clinical model was estimated by a logistic regression model using clinical data. The malignancy probability from the clinical model was used as the best estimate of the pre-test probability of disease to update the malignancy probability of the radiomic model using a nomogram for Bayes' theorem.

RESULTS

The radiomic model had a positive predictive value (PPV) of 86%, an accuracy of 79% and an AUC of 0.67. The clinical model identified DLCO, obstruction index and smoking status as the most consistent clinical predictors associated with outcome. Integrating the clinical features into the deep-learning radiomic model achieves a PPV of 94%, an accuracy of 76% and an AUC of 0.80.

CONCLUSIONS

Incorporating clinical data into a deep-learning radiomic model improved PN malignancy assessment, boosting predictive performance. This study supports the potential of combined image-based and clinical features to improve LC diagnosis.

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.

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.

Incidental pulmonary nodule management in Canada: exploring current state through a narrative literature review and expert interviews

Background and Objective

Incidental pulmonary nodules (IPNs) are common and increasingly detected with the overall rise of radiologic imaging. Effective IPN management is necessary to ensure lung cancer is not missed. This study aims to describe the current landscape of IPN management in Canada, understand barriers to optimal IPN management, and identify opportunities for improvement.

Methods

We performed a narrative literature review by searching biomedical electronic databases for relevant articles published between January 1, 2010, and November 22, 2023. To validate and complement the identified literature, we conducted structured interviews with multidisciplinary experts involved in the pathway of patients with IPNs across Canada. Interviews between December 2021 and May 2022 were audiovisual recorded, transcribed, and thematically analyzed.

Key Content and Findings

A total of 1,299 records were identified, of which 37 studies were included for analysis. Most studies were conducted in Canada and the United States and highlighted variability in radiology reporting of IPNs and patient management, and limited adherence to recommended follow-up imaging. Twenty experts were interviewed, including radiologists, respirologists, thoracic surgeons, primary care physicians, medical oncologists, and an epidemiologist. Three themes emerged from the interviews, supported by the literature, including: variability in radiology reporting of IPNs, suboptimal communication, and variability in guideline adherence and patient management.

Conclusions

Despite general awareness of guidelines, there is inconsistency and lack of standardization in the management of patients with IPNs in Canada. Multidisciplinary expert consensus is recommended to help overcome the communication and operational barriers to a safe and cost-effective approach to this common clinical issue.

Imaging in an academic orthopedic shoulder service: a report on incidental lung pathology findings

INTRODUCTION

Computed tomography (CT) is often utilized for both diagnostic and pre-operative planning purposes in shoulder arthroplasty. Our study reports on the incidence of pulmonary findings in our pre-operative shoulder arthroplasty population over 14 years at our institution.

METHODS

We conducted a retrospective review of all "shoulder CT" exams ordered by two orthopedic upper extremity surgeons between the years of 2008 and 2021. These exams were then further analyzed to include only those ordered for the purpose of pre-operative "shoulder arthroplasty" planning. All incidental findings were documented and those with pulmonary findings were then further analyzed. A detailed chart review was then performed on these patients to determine the impact on their planned shoulder arthroplasty.

RESULTS

A total of 363 shoulder pre-operative CTs were ordered by our two upper extremity orthopedic surgeons at our institution between the years of 2008 and 2021. Primary lung cancer in the form of adenocarcinoma (n = 3) had an incidence of 0.8% of all CT scans and 1.4% of all pulmonary incidental findings. Fifteen patients (4% of all CT scans and 7% of all pulmonary incidental findings) had no concern for malignancy and were appropriately evaluated with further imaging based on their initial shoulder CT.

CONCLUSION

While shoulder arthroplasty and pre-operative planning with CT imaging continue to become more common, so too is the incidence of reported pulmonary findings. From a patient care standpoint, it is important that these findings are accurately identified, appropriately triaged, and communicated clearly to our patients.

CT-guided percutaneous transthoracic needle biopsy (PTNB): A thoracic surgeon's learning curve and experience summary

BACKGROUND

Few studies have investigated the learning process of percutaneous transthoracic needle biopsy (PTNB). Here, we aimed to evaluate the number of cases required to achieve proficiency by plotting the learning curve of PTNB.

METHODS

Data were collected from 94 consecutive patients who underwent computed tomography-guided PTNB by a thoracic surgeon at the Affiliated Hospital of Xuzhou Medical University between May 2021 and February 2022. The data collected included patient information, relevant examination results, intraoperative parameters, postoperative complications, and diagnostic results.

RESULTS

The inflection points of the cumulative sum curve were around cases 13 and 24, according to which three phases were identified, including phase I, phase II, and phase III. A significant downtrend was observed regarding operative time (phase I, 26.53 ± 9.13 min vs. phase III, 18.42 ± 4.29 min, p < 0.05), rate of false-negative (phase I, 15.4% vs. phase III, 5.7%; p < 0.05), rate of pneumothorax (phase I, 30.8% vs. phase III, 12.9%; p < 0.05), and rate of hemoptysis (phase I, 15.4% vs. phase III, 2.9%; p < 0.05).

CONCLUSIONS

Thirteen cases were accumulated to lay the technical foundation, and 24 cases were required to achieve proficiency. In this study we summarize our own experience and provide specific guidance for young doctors with no experience in biopsy.

Associations of systemic inflammation markers with identification of pulmonary nodule and incident lung cancer in Chinese population

Objectives

Neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and systemic immune‐inflammation index (SII), easily accessible systemic inflammation response parameters, were reported to associate with poor lung cancer prognosis. However, research on the effects of these markers on the risk of positive nodules (PNs) and lung cancer is limited.

Methods

Participants in this retrospective study were those who had their first computed tomography (CT) screening at Jiangsu Province Hospital's Health Promotion Center between January 1, 2017 and December 31, 2020. We identified PNs (≥6 mm in diameter) from free text of CT reports and lung cancer from medical records. Multivariate logistic analysis was used to assess the association between NLR, PLR, or SII and PNs or lung cancer.

Results

The detected rate of PNs was 9.60% among the 96,476 participants. Age, smoking and body mass index were possible influencing factors for PNs. We observed linear dose‐effect relationship between NLR, PLR, or SII and PNs (p_non‐linear > 0.05). Compared with low quintile, participants with top quintiles of NLR, PLR or SII had an increased risk of PNs, with the adjusted ORs of 1.19 (1.11–1.28), 1.11 (1.04–1.19) or 1.11 (1.03–1.18), respectively. Meanwhile, NLR showed the U‐shaped relationship with lung cancer, with adjusted ORs of 1.40 (1.08–1.81) comparing highest NLR quintile to the third quintile. The high PLR and SII showed significantly associated with lung cancer with adjusted ORs of 1.29 (0.99–1.68) and 1.35 (1.04–1.74) comparing to the lowest quintile.

Conclusions

The high levels of systemic inflammation markers were associated with the risk of positive pulmonary nodules and lung cancer, which suggested systemic immune response may be an important pre‐clinical feature for the early identification of diseases.