[Artificial intelligence in lung imaging]

Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

OBJECTIVE

To predict the existence of micropapillary or solid components in invasive adenocarcinoma, a model was constructed using qualitative and quantitative features in high-resolution computed tomography (HRCT).

METHODS

Through pathological examinations, 176 lesions were divided into two groups depending on the presence or absence of micropapillary and/or solid components (MP/S): MP/S- group (n = 128) and MP/S + group (n = 48). Multivariate logistic regression analyses were used to identify independent predictors of the MP/S. Artificial intelligence (AI)-assisted diagnostic software was used to automatically identify the lesions and extract corresponding quantitative parameters on CT images. The qualitative, quantitative, and combined models were constructed according to the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) analysis was conducted to evaluate the discrimination capacity of the models with the area under the curve (AUC), sensitivity, and specificity calculated. The calibration and clinical utility of the three models were determined using the calibration curve and decision curve analysis (DCA), respectively. The combined model was visualized in a nomogram.

RESULTS

The multivariate logistic regression analysis using both qualitative and quantitative features indicated that tumor shape (P =  0.029 OR =  4.89; 95% CI 1.175-20.379), pleural indentation (P = 0.039 OR =  1.91; 95% CI 0.791-4.631), and consolidation tumor ratios (CTR) (P < 0.001; OR = 1.05; 95% CI 1.036-1.070) were independent predictors for MP/S + . The areas under the curve (AUC) of the qualitative, quantitative, and combined models in predicting MP/S + were 0.844 (95% CI 0.778-0.909), 0.863 (95% CI 0.803-0.923), and 0.880 (95% CI 0.824-0.937). The combined model of AUC was the most superior and statistically better than qualitative model.

CONCLUSION

The combined model could assist doctors to evaluate patient's prognoses and devise personalized diagnostic and treatment protocols for patients.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis "Expanding Horizons in Radiology"

Diffuse lung disorders (DLDs) and interstitial lung diseases (ILDs) are pathological conditions affecting the lung parenchyma and interstitial network. There are approximately 200 different entities within this category. Radiologists play an increasingly important role in diagnosing and monitoring ILDs, as they can provide non-invasive, rapid, and repeatable assessments using high-resolution computed tomography (HRCT). HRCT offers a detailed view of the lung parenchyma, resembling a low-magnification anatomical preparation from a histological perspective. The intrinsic contrast provided by air in HRCT enables the identification of even the subtlest morphological changes in the lung tissue. By interpreting the findings observed on HRCT, radiologists can make a differential diagnosis and provide a pattern diagnosis in collaboration with the clinical and functional data. The use of quantitative software and artificial intelligence (AI) further enhances the analysis of ILDs, providing an objective and comprehensive evaluation. The integration of "meta-data" such as demographics, laboratory, genomic, metabolomic, and proteomic data through AI could lead to a more comprehensive clinical and instrumental profiling beyond the human eye's capabilities.

Automated semantic lung segmentation in chest CT images using deep neural network

Lung segmentation algorithms play a significant role in segmenting theinfected regions in the lungs. This work aims to develop a computationally efficient and robust deep learning model for lung segmentation using chest computed tomography (CT) images with DeepLabV3 + networks for two-class (background and lung field) and four-class (ground-glass opacities, background, consolidation, and lung field). In this work, we investigate the performance of the DeepLabV3 + network with five pretrained networks: Xception, ResNet-18, Inception-ResNet-v2, MobileNet-v2 and ResNet-50. A publicly available database for COVID-19 that contains 750 chest CT images and corresponding pixel-labeled images are used to develop the deep learning model. The segmentation performance has been assessed using five performance measures: Intersection of Union (IoU), Weighted IoU, Balance F1 score, pixel accu-racy, and global accuracy. The experimental results of this work confirm that the DeepLabV3 + network with ResNet-18 and a batch size of 8 have a higher performance for two-class segmentation. DeepLabV3 + network coupled with ResNet-50 and a batch size of 16 yielded better results for four-class segmentation compared to other pretrained networks. Besides, the ResNet with a fewer number of layers is highly adequate for developing a more robust lung segmentation network with lesser computational complexity compared to the conventional DeepLabV3 + network with Xception. This present work proposes a unified DeepLabV3 + network to delineate the two and four different regions automatically using CT images for CoVID-19 patients. Our developed automated segmented model can be further developed to be used as a clinical diagnosis system for CoVID-19 as well as assist clinicians in providing an accurate second opinion CoVID-19 diagnosis.

Clinical best practices in optimal monitoring, early diagnosis, and effective management of antibody-drug conjugate-induced interstitial lung disease or pneumonitis: a multidisciplinary team approach in Singapore

INTRODUCTION

Interstitial lung disease (ILD) or pneumonitis remains an important adverse event identified with treatment with antibody-drug conjugates (ADCs). Drug-induced ILD (DILD) accounts for 3%-5% of common ILD cases and is a significant problem in clinical practice. Hence, with the anticipation of the widespread use of ADCs, it will be important for guidelines and recommendations to be established to direct and standardize the management of DILD by a multidisciplinary team (MDT).

AREAS COVERED

A thorough literature search was conducted using PubMed to identify relevant articles related to ADCs published between 1 January 2010 and 31 November 2022. Based on the review of the literature combined with expert opinions, this review article offers an overview of incidences of ILDs associated with the use of newer anticancer therapies, specifically ADCs, and discusses local-regional best practices in optimal monitoring, early diagnosis, and management of DILD involving an MDT.

EXPERT OPINION

Multidisciplinary input and consensus are crucial in the accurate diagnosis of DILD. The core group of essential attendees in the MDT are oncologists, pulmonologists, thoracic radiologists, and pathologists. This allows for the integration of expertise from different specialists to achieve a 'best fit' diagnosis and management.

Machine learning classification of mediastinal lymph node metastasis in NSCLC: a multicentre study in a Western European patient population

BACKGROUND

[¹⁸F] FDG PET-CT has an important role in the initial staging of lung cancer; however, accurate differentiation between activity in malignant and benign intrathoracic lymph nodes on PET-CT scans can be challenging. The purpose of the current study was to investigate the effect of incorporating primary tumour data and clinical features to differentiate between [¹⁸F] FDG-avid malignant and benign intrathoracic lymph nodes.

METHODS

We retrospectively selected lung cancer patients who underwent PET-CT for initial staging in two centres in the Netherlands. The primary tumour and suspected lymph node metastases were annotated and cross-referenced with pathology results. Lymph nodes were classified as malignant or benign. From the image data, we extracted radiomic features and trained the classifier model using the extreme gradient boost (XGB) algorithm. Various scenarios were defined by selecting different combinations of data input and clinical features. Data from centre 1 were used for training and validation of the models using the XGB algorithm. To determine the performance of the model in a different hospital, the XGB model was tested using data from centre 2.

RESULTS

Adding primary tumour data resulted in a significant gain in the performance of the trained classifier model. Adding the clinical information about distant metastases did not lead to significant improvement. The performance of the model in the test set (centre 2) was slightly but statistically significantly lower than in the validation set (centre 1).

CONCLUSIONS

Using the XGB algorithm potentially leads to an improved model for the classification of intrathoracic lymph nodes. The inclusion of primary tumour data improved the performance of the model, while additional knowledge of distant metastases did not. In patients in whom metastases are limited to lymph nodes in the thorax, this may reduce costly and invasive procedures such as endobronchial ultrasound or mediastinoscopy procedures.

Correlation of computed tomography quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma

The purpose of the study is to investigate the correlation of computed tomography (CT) quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma.The study involved 141 lesions in 141 patients with early lung adenocarcinoma. According to the degree of tumor invasion, the lesions were assigned into (adenocarcinoma in situ + minimally invasive adenocarcinoma) group and invasive adenocarcinoma (IAC) group. Artificial intelligence-assisted diagnostic software was used to automatically outline the lesions and extract corresponding quantitative parameters on CT images. Statistical analysis was performed to explore the correlation of these parameters with tumor invasion and Ki-67 expression.The results of logistic regression analysis showed that the short diameter of the lesion and the average CT value were independent predictors of IAC. Receiver operating characteristic curve analysis identified the average CT value as an independent predictor of IAC with the best performance, with the area under the receiver operating characteristic curve of 0.893 (P < .001), and the threshold of -450 HU. Besides, the predicted probability of logistic regression analysis model was detected to have the area under the curve of 0.931 (P < .001). The results of Spearman correlation analysis showed that the expression level of Ki-67 had the highest correlation with the average CT value of the lesion (r = 0.403, P < .001).The short diameter of the lesion and the average CT value are independent predictors of IAC, and the average CT value is significantly positively correlated with the expression of tumor Ki-67.

[Artificial intelligence in image evaluation and diagnosis]

Die Radiologie ist von stetem Wandel geprägt und definiert sich über den technologischen Fortschritt. Künstliche Intelligenz (KI) wird die praktische Tätigkeit in der Kinder- und Jugendradiologie künftig in allen Belangen verändern. Bildakquisition, Befunderkennung und -segmentierung sowie die Erkennung von Gewebeeigenschaften und deren Kombination mit Big Data werden die Haupteinsatzgebiete in der Radiologie sein. Höhere Effektivität, Beschleunigung von Untersuchung und Befundung sowie Kosteneinsparung sind mit der Anwendung von KI verbundene Erwartungshaltungen. Ein verbessertes Patientenmanagement, Arbeitserleichterungen für medizinisch-technische Radiologieassistenten und Kinder- und Jugendradiologen sowie schnellere Untersuchungs- und Befundzeiten markieren die Meilensteine der KI-Entwicklung in der Radiologie. Von der Terminkommunikation und Gerätesteuerung bis zu Therapieempfehlung und -monitoring wird der Alltag durch Elemente der KI verändert. Kinder- und Jugendradiologen müssen daher grundlegend über KI informiert sein und mit Datenwissenschaftlern bei der Etablierung und Anwendung von KI-Elementen zusammenarbeiten.

[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

局部热消融技术在肺部结节治疗领域正处在起步与发展阶段，为了肺结节热消融治疗的临床实践和规范发展，由“中国医师协会肿瘤消融治疗技术专家组”“中国医师协会介入医师分会肿瘤消融专业委员会”“中国抗癌协会肿瘤消融治疗专业委员会”“中国临床肿瘤学会消融专家委员会”组织多学科国内有关专家，讨论制定了“热消融治疗肺部亚实性结节专家共识（2021年版）”。主要内容包括：①肺部亚实性结节的临床评估；②热消融治疗肺部亚实性结节技术操作规程、适应证、禁忌证、疗效评价和相关并发症；③存在的问题和未来发展方向。

Factors affecting post-treatment radiation-induced lung disease in patients receiving stereotactic body radiotherapy to lung

The aim of the study is to investigate factors that may cause radiation-induced lung disease (RILD) in patients undergoing stereotactic body radiotherapy (SBRT) for lung tumors. Medical records of patients treated between May 2018 and June 2019 with SBRT were retrospectively evaluated. All patients should have a diagnosis of either primary non-small cell lung cancer (NSCLC) or less than three metastases to lung from another primary. The median treatment dose was 50 Gy in 4-5 fractions. Tumor response and RILD were evaluated in thoracic computer tomography (CT) using RECIST criteria. 82 patients with 97 lung lesions were treated. The median age was 68 years (IQR = 62-76). With a median follow-up of 7.2 months (3-18 months), three patients had grade 3 radiation pneumonitis (RP). RILD was observed in 52% of cases. Patients who had RILD had a higher risk of symptomatic RP (p = 0.007). In multivariate analyses older age, previous lung radiotherapy history, and median planning treatment volume (PTV) D95 value of ≥ 48 Gy were associated with RILD. Local recurrence (LR) was observed in 5.1% of cases. There was no difference in overall survival and LR with the presence of RILD. Older age, previous lung radiotherapy history, and median PTV D95 value of ≥ 48 Gy seems to be associated with post-SBRT RILD.

Artificial Intelligence and its future potential in lung cancer screening

Artificial intelligence (AI) simulates intelligent behavior as well as critical thinking comparable to a human being and can be used to analyze and interpret complex medical data. The application of AI in imaging diagnostics reduces the burden of radiologists and increases the sensitivity of lung cancer screening so that the morbidity and mortality associated with lung cancer can be decreased. In this article, we have tried to evaluate the role of artificial intelligence in lung cancer screening, as well as the future potential and efficiency of AI in the classification of nodules. The relevant studies between 2010-2020 were selected from the PubMed database after excluding animal studies and were analyzed for the contribution of AI. Techniques such as deep learning and machine learning allow automatic characterization and classification of nodules with high precision and promise an advanced lung cancer screening method in the future. Even though several combination models with high performance have been proposed, an effectively validated model for routine use still needs to be improvised. Combining the performance of artificial intelligence with a radiologist's expertise offers a successful outcome with higher accuracy. Thus, we can conclude that higher sensitivity, specificity, and accuracy of lung cancer screening and classification of nodules is possible through the integration of artificial intelligence and radiology. The validation of models and further research is to be carried out to determine the feasibility of this integration.

Progress of Artificial Intelligence in Gynecological Malignant Tumors

Artificial intelligence (AI) is a sort of new technical science which can simulate, extend and expand human intelligence by developing theories, methods and application systems. In the last five years, the application of AI in medical research has become a hot topic in modern science and technology. Gynecological malignant tumors involves a wide range of knowledge, and AI can play an important part in these aspects, such as medical image recognition, auxiliary diagnosis, drug research and development, treatment scheme formulation and other fields. The purpose of this paper is to describe the progress of AI in gynecological malignant tumors and discuss some problems in its application. It is believed that AI improves the efficiency of diagnosis, reduces the burden of clinicians, and improves the effect of treatment and prognosis. AI will play an irreplaceable role in the field of gynecological malignant oncology and will promote the development of medicine and further promote the transformation from traditional medicine to precision medicine and preventive medicine. However, there are also some problems in the application of AI in gynecologic malignant tumors. For example, AI, inseparable from human participation, still needs to be more “humanized”, and needs to further protect patients’ privacy and health, improve legal and insurance protection, and further improve according to local ethnic conditions and national conditions. However, it is believed that with the continuous development of AI, especially ensemble classifier, and deep learning will have a profound influence on the future of medical technology, which is a powerful driving force for future medical innovation and reform.

Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective

Background: Artificial intelligence (AI) has shown promise in numerous experimental studies, particularly in skin cancer diagnostics. Translation of these findings into the clinic is the logical next step. This translation can only be successful if patients' concerns and questions are addressed suitably. We therefore conducted a survey to evaluate the patients' view of artificial intelligence in melanoma diagnostics in Germany, with a particular focus on patients with a history of melanoma.

Participants and Methods: A web-based questionnaire was designed using LimeSurvey, sent by e-mail to university hospitals and melanoma support groups and advertised on social media. The anonymous questionnaire evaluated patients' expectations and concerns toward artificial intelligence in general as well as their attitudes toward different application scenarios. Descriptive analysis was performed with expression of categorical variables as percentages and 95% confidence intervals. Statistical tests were performed to investigate associations between sociodemographic data and selected items of the questionnaire.

Results: 298 individuals (154 with a melanoma diagnosis, 143 without) responded to the questionnaire. About 94% [95% CI = 0.91–0.97] of respondents supported the use of artificial intelligence in medical approaches. 88% [95% CI = 0.85–0.92] would even make their own health data anonymously available for the further development of AI-based applications in medicine. Only 41% [95% CI = 0.35–0.46] of respondents were amenable to the use of artificial intelligence as stand-alone system, 94% [95% CI = 0.92–0.97] to its use as assistance system for physicians. In sub-group analyses, only minor differences were detectable. Respondents with a previous history of melanoma were more amenable to the use of AI applications for early detection even at home. They would prefer an application scenario where physician and AI classify the lesions independently. With respect to AI-based applications in medicine, patients were concerned about insufficient data protection, impersonality and susceptibility to errors, but expected faster, more precise and unbiased diagnostics, less diagnostic errors and support for physicians.

Conclusions: The vast majority of participants exhibited a positive attitude toward the use of artificial intelligence in melanoma diagnostics, especially as an assistance system.