https://www.hal.inserm.fr/inserm-00561753/document

International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma

Advances in genomics, proteomics and molecular pathology have generated many candidate biomarkers with potential clinical value. Their use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. However, translation from bench to bedside outside of the research setting has proved more difficult than might have been expected. Understanding how and when biomarkers can be integrated into clinical care is crucial if we want to translate the promise into reality.

Biomarkers in cancer staging, prognosis and treatment selection.

This report is to complement the original Fleischner Society recommendations for incidentally detected solid nodules by proposing a set of recommendations specifically aimed at subsolid nodules. The development of a standardized approach to the interpretation and management of subsolid nodules remains critically important given that peripheral adenocarcinomas represent the most common type of lung cancer, with evidence of increasing frequency. Following an initial consideration of appropriate terminology to describe subsolid nodules and a brief review of the new classification system for peripheral lung adenocarcinomas sponsored by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS), six specific recommendations were made, three with regard to solitary subsolid nodules and three with regard to multiple subsolid nodules. Each recommendation is followed first by the rationales underlying the recommendation and then by specific pertinent remarks. Finally, issues for which future research is needed are discussed. The recommendations are the result of careful review of the literature now available regarding subsolid nodules. Given the complexity of these lesions, the current recommendations are more varied than the original Fleischner Society guidelines for solid nodules. It cannot be overemphasized that these guidelines must be interpreted in light of an individual's clinical history. Given the frequency with which subsolid nodules are encountered in daily clinical practice, and notwithstanding continuing controversy on many of these issues, it is anticipated that further refinements and modifications to these recommendations will be forthcoming as information continues to emerge from ongoing research.

https://pubs.rsna.org/doi/pdf/10.1148/radiol.12120628

Recommendations for the Management of Subsolid Pulmonary Nodules Detected at CT: A Statement from the Fleischner Society

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features.

Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

Current computed tomography (CT) technology allows for near isotropic, submillimeter resolution acquisition of the complete chest in a single breath hold. These thin-slice chest scans have become indispensable in thoracic radiology, but have also substantially increased the data load for radiologists. Automating the analysis of such data is, therefore, a necessity and this has created a rapidly developing research area in medical imaging. This paper presents a review of the literature on computer analysis of the lungs in CT scans and addresses segmentation of various pulmonary structures, registration of chest scans, and applications aimed at detection, classification and quantification of chest abnormalities. In addition, research trends and challenges are identified and directions for future research are discussed.

Computer analysis of computed tomography scans of the lung: a survey

Purpose: To evaluate the effect of a computer-aided diagnosis (CAD) system on the diagnostic performance of radiologists for the estimation of the malignancy of pulmonary nodules on thin-section helical computed tomographic (CT) scans. Materials and Methods: The institutional review board approved use of the CT database; informed specific study-related consent was waived. The institutional review board approved participation of radiologists; informed consent was obtained from all observers. Thirty-three (18 malignant, 15 benign) pulmonary nodules of less than 3.0 cm in maximal diameter were evaluated. Receiver operating characteristic (ROC) analysis with a continuous rating scale was used to compare observer performance for the estimation of the likelihood of malignancy first without and then with the CAD system. The participants were 10 board-certified radiologists and nine radiology residents. Results: For all 19 participants, the mean area under the best-fit ROC curve (Az) values achieved without and w...

Pulmonary Nodules: Estimation of Malignancy at Thin-Section Helical CT—Effect of Computer-aided Diagnosis on Performance of Radiologists

OBJECTIVE. The purpose of this study was to investigate the accuracy and reproducibility of results acquired with computer-aided volumetry software during MDCT of pulmonary nodules exhibiting ground-glass opacity.MATERIALS AND METHODS. To evaluate the accuracy of computer-aided volumetry software, we performed thin-section helical CT of a chest phantom that included simulated 3-, 5-, 8-, 10-, and 12-mm-diameter ground-glass opacity nodules with attenuation of –800, –630, and –450 HU. Three radiologists measured the volume of the nodules and calculated the relative volume measurement error, which was defined as follows: (measured nodule volume minus assumed nodule volume ÷ assumed nodule volume) × 100. Two radiologists performed two independent measurements of 59 nodules in humans. Intraobserver and interobserver agreement was evaluated with Bland-Altman methods.RESULTS. The relative volume measurement error for simulated ground-glass opacity nodules measuring 3 mm ranged from 51.1% to 85.2% and for nodule...

/pdf/computer-aided-volumetry-of-pulmonary-nodules-exhibiting-55tbskrovt.pdf

Computer-Aided Volumetry of Pulmonary Nodules Exhibiting Ground-Glass Opacity at MDCT

An original image is subjected to an opening processing and to a closing processing to obtain a blurred image by using an average value between the result of the opening processing and the result of the closing processing as a reference for correcting the brightness. The brightness of the original image is corrected by multiplying a difference between the blurred image and the original image, by a ratio of an upper limit value of brightness to a difference between the upper limit value of brightness and the blurred image. Then, the brightness of the original image is corrected based on the blurred image which enhances the contrast of a highly bright portion of a predetermined area or more while lowering the brightness of that portion. Contrast and brightness of the whole image can be adapted to the sensitivity of human eyes.

Image processing apparatus and image processing program

In a cross-sectional image displayed on a display screen, a region of interest is designated by a user. In the designated region of interest, a two-dimensional projected image that three-dimensionally represents the cross-sectional image inside the region of interest is displayed. Particularly, a two-dimensional projected image of a tumor three-dimensionally representing an image of a tumor is displayed. An image representing even a depth of a region that the user desires to locally view and a three-dimensional image positioned in a cross-sectional image can be displayed, thereby allowing a morphological feature of a lesion to be easily grasped.

Apparatus for displaying cross-sectional image and computer product

PROBLEM TO BE SOLVED: To easily and intuitively recognize the position relation of a two-dimensional projection image that a user wants to view locally and a cross-sectional image around it. SOLUTION: In the display region 1001 of a display screen 1000, the cross-sectional image 1002 of a living body is displayed. In the cross-sectional image 1002, a region of interest ROI is specified by the user, and the two-dimensional projection image 1103 stereoscopically expressing the cross-sectional image 1003 inside the region of interest ROI is displayed in the specified region of interest ROI. Especially, in the region of interest ROI, the two-dimensional projection image T of a tumor three-dimensionally displaying a tumor image T is displayed. Thus, an image expressing even the depth for the region (region of interest ROI) that the user wants to view locally and a stereoscopic image positioned on a cross section can be viewed, and the morphologic feature of a lesion is recognized easily compared to the cross-sectional image. COPYRIGHT: (C)2006,JPO&NCIPI

Image display device and image display program

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