Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening

Lung cancer is the leading cause of cancer related mortality worldwide. Despite advancements in its treatment, patients with late-stage lung cancer still have poor outcome. Lung cancer screening using low-dose computed tomography (CT) scans has been shown to improve survival. However, early lung cancer screening trials using diameter-based lung nodule management methods were plagued by a high rate of false positive screen results, which may result in patient harm and increased healthcare cost. The Dutch-Belgian Lung Cancer Screening Trial (Dutch acronym NELSON) is the first lung cancer screening trial in which the nodule management method primarily is based on volume and growth rate, instead of diameter. This has led to a reduction in the number of false-positive results. Due to the positive results from the NELSON trial, the implementation of screening is being considered in Europe. Prerequisite of wide implementation of lung cancer screening includes accurate distinction of malignant nodules from benign lesions and safety relating to exposure to ionizing radiation. In this thesis more insight is provided in into (1) the comparison of volume- and diameter-based nodule quantification methods in lung cancer screening, (2) the use of ultra-low dose CT to reduce ionizing radiation to screening participants, (3) the classification and exclusion of benign nodules from screening participants with pulmonary nodules

False Negative/Positive Control for SAM on Noisy Medical Images

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon

Evaluation of a novel deep learning-based classifier for perifissural nodules

OBJECTIVES: To evaluate the performance of a novel convolutional neural network (CNN) for the classification of typical perifissural nodules (PFN). METHODS: Chest CT data from two centers in the UK and The Netherlands (1668 unique nodules, 1260 individuals) were collected. Pulmonary nodules were classified into subtypes, including "typical PFNs" on-site, and were reviewed by a central clinician. The dataset was divided into a training/cross-validation set of 1557 nodules (1103 individuals) and a test set of 196 nodules (158 individuals). For the test set, three radiologically trained readers classified the nodules into three nodule categories: typical PFN, atypical PFN, and non-PFN. The consensus of the three readers was used as reference to evaluate the performance of the PFN-CNN. Typical PFNs were considered as positive results, and atypical PFNs and non-PFNs were grouped as negative results. PFN-CNN performance was evaluated using the ROC curve, confusion matrix, and Cohen's kappa. RESULTS: Internal validation yielded a mean AUC of 91.9% (95% CI 90.6-92.9) with 78.7% sensitivity and 90.4% specificity. For the test set, the reader consensus rated 45/196 (23%) of nodules as typical PFN. The classifier-reader agreement (k = 0.62-0.75) was similar to the inter-reader agreement (k = 0.64-0.79). Area under the ROC curve was 95.8% (95% CI 93.3-98.4), with a sensitivity of 95.6% (95% CI 84.9-99.5), and specificity of 88.1% (95% CI 81.8-92.8). CONCLUSION: The PFN-CNN showed excellent performance in classifying typical PFNs. Its agreement with radiologically trained readers is within the range of inter-reader agreement. Thus, the CNN-based system has potential in clinical and screening settings to rule out perifissural nodules and increase reader efficiency. KEY POINTS: • Agreement between the PFN-CNN and radiologically trained readers is within the range of inter-reader agreement. • The CNN model for the classification of typical PFNs achieved an AUC of 95.8% (95% CI 93.3-98.4) with 95.6% (95% CI 84.9-99.5) sensitivity and 88.1% (95% CI 81.8-92.8) specificity compared to the consensus of three readers

Clinical characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital

Objectives: To evaluate the characteristics and work-up of small to intermediate-sized pulmonary nodules in a Chinese dedicated cancer hospital. Methods: Patients with pulmonary nodules 4-25 mm in diameter detected via computed tomography (CT) in 2013 were consecutively included. The analysis was restricted to patients with a histological nodule diagnosis or a 2-year follow-up period without nodule growth confirming benign disease. Patient information was collected from hospital records. Results: Among the 314 nodules examined in 299 patients, 212 (67.5%) nodules in 206 (68.9%) patients were malignant. Compared to benign nodules, malignant nodules were larger (18.0 mm vs. 12.5 mm, P < 0.001), more often partly solid (16.0% vs. 4.7%, P < 0.001) and more often spiculated (72.2% vs. 41.2%, P < 0.001), with higher density in contrast-enhanced CT (67.0 HU vs. 57.5 HU, P = 0.015). Final diagnosis was based on surgery in 232 out of 314 (73.9%) nodules, 166 of which were identified as malignant [30 (18.1%) stage III or IV] and 66 as benign. In 36 nodules (11.5%), diagnosis was confirmed by biopsy and the remainder verified based on stability of nodule size at follow-up imaging (n = 46, 14.6%). Among 65 nodules subjected to gene (EGFR) mutation analyses, 28 (43.1%) cases (EGFR19 n = 13; EGFR21 n = 15) were identified as EGFR mutant and 37 (56.9%) as EGFR wild-type. Prior to surgery, the majority of patients [n = 194 (83.6%)] received a contrast-enhanced CT scan for staging of both malignant [n = 140 (84.3%)] and benign [n = 54 (81.8%)] nodules. Usage of positron emission tomography (PET)-CT was relatively uncommon [n = 38 (16.4%)]. Conclusions: CT-derived nodule assessment assists in diagnosis of small to intermediate- sized malignant pulmonary nodules. Currently, contrast-enhanced CT is commonly used as the sole diagnostic confirmation technique for pre-surgical staging, often resulting in surgery for late-stage disease and unnecessary surgery in cases of benign nodules

Comparison of Veterans Affairs, Mayo, Brock classification models and radiologist diagnosis for classifying the malignancy of pulmonary nodules in Chinese clinical population

Background: Several classification models based on Western population have been developed to help clinicians to classify the malignancy probability of pulmonary nodules. However, the diagnostic performance of these Western models in Chinese population is unknown. This paper aimed to compare the diagnostic performance of radiologist evaluation of malignancy probability and three classification models (Mayo Clinic, Veterans Affairs, and Brock University) in Chinese clinical pulmonology patients. Methods: This single-center retrospective study included clinical patients from Tianjin Medical University Cancer Institute and Hospital with new, CT-detected pulmonary nodules in 2013. Patients with a nodule with diameter of 4-25 mm, and histological diagnosis or 2-year follow-up were included. Analysis of area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and threshold of decision analysis was used to evaluate the diagnostic performance of radiologist diagnosis and the three classification models, with histological diagnosis or 2-year follow-up as the reference. Results: In total, 277 patients (286 nodules) were included. Two hundred and seven of 286 nodules (72.4%) in 203 patients were malignant. AUC of the Mayo model (0.77; 95% CI: 0.72-0.82) and Brock model (0.77; 95% CI: 0.72-0.82) were similar to radiologist diagnosis (0.78; 95% CI: 0.73-0.83; P=0.68, P=0.71, respectively). The diagnostic performance of the VA model (AUC: 0.66) was significantly lower than that of radiologist diagnosis (P=0.003). A three-class classifying threshold analysis and DCA showed that the radiologist evaluation had higher discriminatory power for malignancy than the three classification models. Conclusions: In a cohort of Chinese clinical pulmonology patients, radiologist evaluation of lung nodule malignancy probability demonstrated higher diagnostic performance than Mayo, Brock, and VA classification models. To optimize nodule diagnosis and management, a new model with more radiological characteristics could be valuable

A Subsolid Nodules Imaging Reporting System (SSN-IRS) for Classifying 3 Subtypes of Pulmonary Adenocarcinoma

It is essential to identify the subsolid nodules subtype preoperatively to select the optimal treatment algorithm. We developed and validated an imaging reporting system using a classification and regression tree model that based on computed tomography imaging characteristics (291 cases in training group, 146 cases in testing group). The model showed high sensitivity and accuracy of classification. Our model can help clinicians to make follow-up recommendations or decisions for surgery for clinical patients with a subsolid nodule

New Fissure-Attached Nodules in Lung Cancer Screening:A Brief Report From The NELSON Study

Introduction: In incidence lung cancer screening rounds, new pulmonary nodules are regular findings. They have a higher lung cancer probability than baseline nodules. Previous studies have shown that baseline perifissural nodules (PFNs) represent benign lesions. Whether this is also the case for incident PFNs is unknown. This study evaluated newly detected nodules in the Dutch-Belgian randomized-controlled NELSON study with respect to incidence of fissure-attached nodules, their classification, and lung cancer probability. Methods: Within the NELSON trial, 7557 participants underwent baseline screening between April 2004 and December 2006. Participants with new nodules detected after baseline were included. Nodules were classified based on location and attachment. Fissure-attached nodules were re-evaluated to be classified as typical, atypical, or non-PFN by two radiologists without knowledge of participant lung cancer status. Results: One thousand four hundred eighty-four new nodules were detected in 949 participants (77.4% male, median age 59 years [interquartile range: 55–63 years]) in the second, third, and final NELSON screening round. Based on 2-year follow-up or pathology, 1393 nodules (93.8%) were benign. In total, 97 (6.5%) were fissure-attached, including 10 malignant nodules. None of the new fissure-attached malignant nodules was classified as typical or atypical PFN. Conclusions: In the NELSON study, 6.5% of incident lung nodules were fissure-attached. None of the lung cancers that originated from a new fissure-attached nodule in the incidence lung cancer screening rounds was classified as a typical or atypical PFN. Our results suggest that also in the case of a new PFN, it is highly unlikely that these PFNs will be diagnosed as lung cancer

Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening

OBJECTIVE: To evaluate the influence of nodule margin on inter- and intra-reader variability in manual diameter measurements and semi-automatic volume measurements of solid nodules detected in low-dose CT lung cancer screening. METHODS: Twenty-five nodules of each morphological category (smooth, lobulated, spiculated and irregular) were randomly selected from 93 participants of the Dutch-Belgian randomized lung cancer screening trial (NELSON). Semi-automatic volume measurements were performed using Syngo LungCARE® software. Three radiologists independently measured mean diameters manually. Impact of nodule margin on inter-reader variability was evaluated based on systematic error and 95% limits of agreement. Inter-reader variability was compared to the nodule growth cutoff as used in Lung-RADS (+1.5mm diameter) and NELSON/British Thoracic Society (+25% volume). RESULTS: For manual diameter measurements, a significant systematic error (up to 1.2mm) between readers was found in all morphological categories. For semi-automatic volume measurements, no statistically significant systematic error was found. The inter-reader variability in mean diameter measurements exceeded the 1.5mm cut-off for nodule growth for all morphological categories (smooth: ±1.9mm [+27%], lobulated: ±2.0mm [+33%], spiculated: ±3.5mm [+133%], irregular: ±4.5mm [+200%]). The 25%-volume growth cut-off was exceeded slightly for spiculated (28% [+12%]) and irregular (27% [+8%]) nodules. CONCLUSION: Lung nodule sizing based on manual diameter measurement is affected by nodule margin. Inter-reader variability increases especially for nodules with spiculated and irregular margins, and may cause misclassification of nodule growth. This effect is much smaller for semi-automated volume measurements. Advances in knowledge: Semi-automatic volume measurements are superior for both size and growth determination of pulmonary nodules

Lung cancer prediction by Deep Learning to identify benign lung nodules

INTRODUCTION: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity. METHODS: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.S. National Lung Screening Trial (NLST), and validated on CT scans containing 2106 nodules (205 lung cancers) detected in patients from from the Early Lung Cancer Diagnosis Using Artificial Intelligence and Big Data (LUCINDA) study, recruited from three tertiary referral centers in the UK, Germany and Netherlands. We pre-defined a benign nodule rule-out test, to identify benign nodules whilst maintaining a high sensitivity, by calculating thresholds on the malignancy score that achieve at least 99 % sensitivity on the NLST data. Overall performance per validation site was evaluated using Area-Under-the-ROC-Curve analysis (AUC). RESULTS: The overall AUC across the European centers was 94.5 % (95 %CI 92.6-96.1). With a high sensitivity of 99.0 %, malignancy could be ruled out in 22.1 % of the nodules, enabling 18.5 % of the patients to avoid follow-up scans. The two false-negative results both represented small typical carcinoids. CONCLUSION: The LCP-CNN, trained on participants with lung nodules from the US NLST dataset, showed excellent performance on identification of benign lung nodules in a multi-center external dataset, ruling out malignancy with high accuracy in about one fifth of the patients with 5-15 mm nodules

An Update on the European Lung Cancer Screening Trials and Comparison of Lung Cancer Screening Recommendations in Europe

While lung cancer screening has been implemented in the United States, it is still under consideration in Europe. So far, lung cancer screening trials in Europe were not able to replicate the results of the National Lung Screening Trial, but they do show a stage shift in the lung cancers that were detected. While eagerly awaiting the final result of the only lung cancer screening trial with sufficient statistical power, the NELSON trial, a number of European countries and medical societies have published recommendations for lung cancer screening using computed tomography. However, there is still a debate with regard to the design of future lung cancer screening programs in Europe. This review summarizes the latest evidence of European lung cancer screening trials and gives an overview of the essence of recommendations from the different European medical societies and countries