Künstliche Intelligenz in der Bildgebung der Lunge

Prayer, Florian; Röhrich, Sebastian; Pan, Jeanny; Hofmanninger, Johannes; Prosch, Helmut

doi:10.1007/s00117-019-00611-2

Cited by 15 publications

(8 citation statements)

References 31 publications

(24 reference statements)

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“…However, the experience of the reading physician makes certain effects since the measurement is tedious and time-consuming, besides, the size and density of the lesion measured manually lack accuracy and repeatability. [15,16] In this study, AI-assisted diagnostic software was used. It can directly and automatically outline the lesion and accurately quantify its various indicators, which reduces the error of manual measurement and has high repeatability and reliability.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.Abbreviations: AAH = adenomatous hyperplasia, AI = artificial intelligence, AIS = adenocarcinoma in situ, AUC = area under the curve, CT = computed tomography, HRCT = high-resolution computed tomography, IAC = invasive adenocarcinoma, LUAD = lung adenocarcinoma, MIA = minimally invasive adenocarcinoma, ROC = receiver operating characteristic.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…DL and Radiomics are creating a paradigm shift in radiology and precision medicine by developing a new area of research to be used for precision medicine. 12 For example, AI can be useful in the detection of lung nodules from CT images, [13][14][15] prediction of complete response after neoadjuvant chemoradiation for locally advanced rectal cancer, 16 prediction of the response to individual induction chemotherapy in advanced nasopharyngeal carcinoma, 17 as well as the detection of cell mitosis, 18 and lymph node metastasis 19 in breast cancer from pathological images, etc. In recent years, with the further penetration of AI into the field of medical treatment, gynecologic oncologists are unwilling to lag behind.…”

Section: The Background Of Ai In the Application Of Medical And Gynecmentioning

confidence: 99%

<p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>

Jie

Zeng

2020

CMAR

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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.

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“…Non-small cell lung cancer (NSCLC) is one of the most prevalent malignancies in the western world; PET-CT has an important role in its initial staging [1][2][3][4][5]. The use of [ 18 F] FDG PET-CT for preoperative staging of NSCLC reduces both the total number of thoracotomies and the number of unnecessary thoracotomies [6][7][8][9]. It has high sensitivity and intermediate specificity for the detection of primary tumours, locoregional lymph node metastases, and distant metastases.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence has the potential to improve diagnostic accuracy and management in patients with NSCLC [17][18][19][20], and specifically for use with CT imaging [21][22][23]. Wang et al [24] demonstrated that a classification model for PET-CT, based on machine learning (ML) methods, can be highly accurate and perform on par with or better than human observers in differentiating between malignant and benign intrathoracic lymph nodes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Künstliche Intelligenz in der Bildgebung der Lunge

Cited by 15 publications

References 31 publications

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

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

<p>Progress of Artificial Intelligence in Gynecological Malignant Tumors</p>

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

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