Decision making in patients with pulmonary nodules

Application and evaluation of NCCN guidelines in health education for lung nodule screening: A perspective

This study investigates the application and evaluation of National Comprehensive Cancer Network (NCCN) guidelines within health education frameworks aimed at lung nodule screening. Through the integration of NCCN directives, tailored educational strategies catering to diverse demographics, and robust interdisciplinary collaboration, the research underscores the pivotal role of health education in optimizing screening efficacy and patient outcomes. Moreover, it critically analyzes the challenges encountered, offering insightful recommendations for future research and practice while avoiding replication of existing literature. This study contributes to the field with scholarly rigor, emphasizing the imperative of continuous education in improving patient care standards and mitigating the burden of lung cancer.

The Winner and still champion: Nodule volume doubling times

There have been enormous advances in the approach to assessing malignancy status of indeterminate pulmonary nodules including risk models, image based biomarkers and numerous types of biologic and molecular markers. All of these have the advantage of guiding further workup once the nodule is identified. The traditional method, especially for smaller nodules relies primarily on assessing whether a nodule changes in size over time and is a feature in virtually every management protocol for both screen detected as well as incidentally detected nodules. Here, the potential downside is that during the waiting period for obtaining a second scan to assess for growth prognosis changes. However, there must be enough of a time delay to overcome potential measurement error. These two features must be balanced for optimal use of this approach. The alternative approaches do not have this inherent delay, however, their usefulness is a balance between the improvement in prognosis by not having any delays versus their potential to produce false positive and false negative results. Currently nodule volumetric approaches, especially for small nodules remains the method of choice for evaluation.

Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI

BACKGROUND

The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities.

PURPOSE

To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules.

STUDY TYPE

Prospective.

POPULATION

54 patients, including 21 men and 33 women.

FIELD STRENGTH/SEQUENCE

Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo.

ASSESSMENT

Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded.

STATISTICAL TESTS

The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant.

RESULTS

A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements.

DATA CONCLUSION

The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Comparing Quality of Life and Psychological Changes in Benign and Malignant Lung Resections

Background and Objectives: Pulmonary resections are critical interventions for treating various lung pathologies, both benign and malignant. Understanding the impact of these surgeries on patients' Quality of Life (QoL) is essential for optimizing care. This study aims to compare the Health-Related Quality of Life (HRQoL) and psychological well-being in patients who underwent pulmonary resections for benign versus malignant etiologies. Methods: A cross-sectional study was conducted involving 117 patients who underwent pulmonary resection between January 2022 and June 2023. Participants were divided into two groups: 51 patients with benign lung conditions and 66 with malignant lung tumors. HRQoL was assessed using the SF-36 and WHOQOL-BREF questionnaires. Anxiety and depression levels were evaluated using the Hospital Anxiety and Depression Scale (HADS) and the Perceived Stress Scale (PSS-10). Patients were assessed pre- and post-intervention. Results: Patients with malignant etiologies were older (58.7 vs. 54.2 years) and had lower FEV1% predicted (79.1% vs. 82.5%) compared to the benign group. Malignant patients reported significantly lower scores in physical functioning (68.1 vs. 75.4), role-physical (65.0 vs. 72.3), and general health domains of the SF-36 (62.4 vs. 70.2). WHOQOL-BREF scores indicated a lower overall QoL in the malignant group, particularly in the physical health (65.3 vs. 72.1) and psychological domains (68.0 vs. 74.5). HADS scores revealed higher anxiety (9.1 vs. 7.2) and depression levels (8.5 vs. 6.8) among malignant patients. Correlation analyses showed strong associations between lower QoL scores and higher anxiety and depression levels. Conclusions: Pulmonary resections for malignant conditions are associated with a significant decline in HRQoL compared to benign conditions. Patients with malignant etiologies experience higher levels of anxiety and depression, emphasizing that clinicians should integrate specialized mental health services and tailored physical rehabilitation programs for patients undergoing pulmonary resections for malignant lung conditions to address their significantly reduced quality of life and increased psychological distress.

Improving Cancer Probability Estimation in Nondiagnostic Bronchoscopies: A Meta-Analysis

BACKGROUND

In patients with peripheral pulmonary lesions (PPLs), nondiagnostic bronchoscopy results are not uncommon. The conventional approach to estimate the probability of cancer (pCA) after bronchoscopy relies on dichotomous test assumptions, using prevalence, sensitivity, and specificity to determine negative predictive value. However, bronchoscopy is a multidisease test, raising concerns about the accuracy of dichotomous methods.

RESEARCH QUESTION

By how much does calculating pCA using a dichotomous approach (pCAdichotomous) underestimate the true pCA when applied to multidisease tests like bronchoscopy for the diagnosis of PPL?

METHODS

In this meta-analysis of cohort studies involving radial endobronchial ultrasound for PPL, Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed, constructing 2 × 2 contingency tables for calculating pCAdichotomous. For the multidisease test approach, 3 × 3 contingency tables for calculating probability of malignancy for a test that can have different categories of results and can diagnose multiple diseases (pCAmultidisease) using the likelihood ratio (LR) method for nondiagnostic results (LR(T0)) was used. Observed malignancy rates in patients with nondiagnostic results were compared with pCAdichotomous and pCAmultidisease.

RESULTS

In 46 studies (7,506 patients), malignancy was the underlying diagnosis in 76% of cases, another specific disease in 13% of cases, and nonspecific fibrosis or scar in 10% of cases. The percentage of patients with nondiagnostic results who had malignancy matched pCAmultidisease across all studies. In contrast, pCAdichotomous consistently underestimated cancer risk (median difference, 0.12; interquartile range, 0.06-0.23), particularly in studies with a higher prevalence of nonmalignant disease. The pooled LR(T0) was 0.46 (95% CI, 0.40-0.52; I2 = 76%; P < .001) and correlated with the prevalence of nonmalignant diseases (P = .001).

INTERPRETATION

Conventional dichotomous methods for estimating pCA after nondiagnostic bronchoscopies underestimate the likelihood of malignancy. Physicians should opt for the multidisease test approach when interpreting bronchoscopy results.

Impact on Prognosis of Stage I Non-Small Cell Lung Cancer Secondary to Delays in Diagnostic Workup

Background Diagnostic workup of small pulmonary nodules often requires follow-up CT scans to confirm nodule growth before invasive diagnostics or treatment. Purpose To confirm prior results from the International Early Lung Cancer Action Program (I-ELCAP) on quantifying decreases in lung cancer prognosis by using two large databases, the National Lung Screening Trial (NLST) and International Association for the Study of Lung Cancer (IASLC). Materials and Methods In this retrospective study, a model was developed to predict cure rates based on size of solid nodules using the NLST (August 2002 to summer 2007) and IASLC (January 2011 to December 2019) databases, focusing on stage I non-small cell lung cancer (NSCLC). Kaplan-Meier methods were used to calculate 10-year lung cancer-specific survival and 5-year overall survival rates for different tumor sizes. Tumor diameter increases after 90-, 180-, and 365-day delays were estimated using volume doubling times (VDTs) of 60, 120, and 240 days corresponding to fast, moderate, and slow tumor growth. Initial and delayed lung cancer cure rates were assessed across nine scenarios of time delays and tumor growth rates and compared with the previous results of the I-ELCAP database. Results Using regression models based on 166 NLST and 22 590 IASLC patients with NSCLC, 10-year lung cancer-specific survival and 5-year overall survival, respectively, for tumors 4.0-20.0 mm in diameter were estimated. For a 20.0-mm tumor with a 60-day VDT in the NLST database, the lung cancer-specific survival decreased from 83.4% to 76.5%, 66.8%, and 32.3% after 90, 180, and 365 days, respectively. The IASLC database showed similar decreases in 5-year overall survival, from 81.2% to 73.4%, 62.4%, and 23.3% after 90, 180, and 365 days, respectively. Comparison across NLST, IASLC, and I-ELCAP databases revealed minor variations in lung cancer cure rates between 79.9% and 83.4%, with reductions of 6.9%-8.3% after a 180-day delay with a 120-day VDT. Conclusion The NLST and IASLC databases confirmed prior estimates from the I-ELCAP database for the decrease in lung cancer prognosis due to diagnostic delays. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Decision threshold models in medical decision making: a scoping literature review

BACKGROUND

Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds.

METHODS

A structured search designed around three concepts-individual decision-maker, decision threshold, and medical decision-was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision.

RESULTS

Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles.

CONCLUSIONS

Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

BACKGROUND

To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.

METHODS

This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method.

RESULTS

The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively.

CONCLUSIONS

A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules

BACKGROUND

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

METHODS

We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses.

RESULTS

Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model.

CONCLUSIONS

Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

Assessing the accuracy of a multisection robotic bronchoscope prototype in localization and targeting of small pulmonary lesions

Objectives

Robotic bronchoscopy (RB) has emerged as a novel technique to address issues with the biopsy of small peripheral lung lesions. The objective of this study was to quantitatively assess the accuracy of a novel multisection robotic bronchoscope compared with current standards of care.

Methods

This is a prospective, single-blind, comparative study where the accuracy of a multisection RB was compared against the accuracy of standard electromagnetic navigational bronchoscopy (EM-NB) during lesion localization and targeting. Five blinded subjects of varying bronchoscopy experience were recruited to use both RB and EM-NB in a swine lung model. Accuracy of localization and targeting success was measured as the distance from the center of pulmonary targets at each anatomic location. Subjects used both RB and EM-NB to navigate to 4 pulmonary targets assigned using 1:1 block randomization. Differences in accuracy and time between navigation systems were assessed using Wilcoxon rank-sum test.

Results

Of the 40 total attempts per modality, successful targeting was achieved on 90% and 85% of attempts utilizing RB and EM-NB, respectively. Furthermore, RB demonstrated significantly lower median distance to the real-time EM target (1.1 mm; interquartile range [IQR], 0.6-2.0 mm) compared with EM-NB (2.6 mm; IQR, 1.6-3.8) (P < .001). Median target displacement resulting from lung and bronchus deformation during bronchoscopy was found to be significantly lower using RB (0.8 mm; IQR, 0.5-1.2 mm) compared with EM-NB (2.6 mm; IQR, 1.4-6.4 mm) (P < .001).

Conclusions

The results of this study demonstrate that the multi-section RB prototype allows for improved localization and targeting of small peripheral lung nodules compared with current nonrobot bronchoscopy modalities.

Characterizing the accuracy of robotic bronchoscopy in localization & targeting of small pulmonary lesions

PURPOSE

Considering the recent implementation of lung cancer screening guidelines, it is crucial that small pulmonary nodules are accurately diagnosed. There is a significant need for quick, precise, and minimally invasive biopsy methods, especially for patients with small lung lesions in the outer periphery. Robotic bronchoscopy (RB) has recently emerged as a novel solution. The purpose of this study was to evaluate the accuracy of RB compared to the existing standard, electromagnetic navigational bronchoscopy (EM-NB).

METHODS

A prospective, single-blinded, and randomized-controlled study was performed to compare the accuracy of RB to EM-NB in localizing and targeting pulmonary lesions in a porcine lung model. Four operators were tasked with navigating to four pulmonary targets in the outer periphery of a porcine lung, to which they were blinded, using both the RB and EM-NB systems. The dependent variable was accuracy. Accuracy was measured as a rate of success in lesion localization and targeting, the distance from the center of the pulmonary target, and by anatomic location. The independent variable was the navigation system, RB was compared to EM-NB using 1:1 randomization.

RESULTS

Of 75 attempts, 72 were successful in lesion localization and 60 were successful in lesion targeting. The success rate for lesion localization was 100% with RB and 91% with EM- NB. The success rate for lesion targeting was 93% with RB and 80% for EM-NB. RB demonstrated superior accuracy in reaching the distance from the center of the lesion, at 0.62 mm compared to EM-NB at 1.28 mm (p = 0.001). Accuracy was improved using RB compared to EM- NB for lesions in the LLL (p = 0.025), LUL (p < 0.001), and RUL (p < 0.001).

CONCLUSION

Our findings support RB as a more accurate method of navigating and localizing small peripheral pulmonary targets when compared to standard EM-NB in a porcine lung model. This may be attributed to the ability of RB to reduce substantial tissue displacement seen with standard EM-NB navigation. As the development and application of RB advances, so will the ability to accurately diagnose small peripheral lung cancer nodules, providing patients with early-stage lung cancer the best possible outcomes.

Shared decision-making in the management of pulmonary nodules: a systematic review of quantitative and qualitative studies

OBJECTIVE

The objective of this systematic review was to explore the evidence regarding shared decision-making (SDM) in the management of pulmonary nodules.

DESIGN

Systematic review of quantitative and qualitative studies.

DATA SOURCE

Studies published in English or Chinese up to April 2022 were extracted from nine databases: PubMed, PsycINFO, EMBASE, Cochrane Library, Web of Science and CINAHL, China National Knowledge Infrastructure, Wanfang Data and SinoMed Data.

ELIGIBILITY CRITERIA

Studies were eligible if patients or healthcare providers are faced with pulmonary nodule management options or the interventions or experiences were focused on the patient-healthcare provider relationship or health education to make, increase or support shared decisions. All types of studies were included, including quantitative and qualitative studies. Grey literature and literature that had not been peer reviewed were excluded. Poster abstracts and non-empirical publications such as editorials, letters, opinion papers and review articles were excluded.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently screened abstracts and full texts, assessed quality using Joanna Briggs Institute's critical appraisal tools, and extracted data from included studies. Thematic syntheses were used to identify prominent themes emerging from the data.

RESULTS

A total of 12 studies met the inclusion criteria, 11 of which were conducted in USA. These included six qualitative studies and six quantitative studies (including both survey and quasi-experimental designs). Three major themes with specific subthemes emerged: (1) Opportunity (uncertainty in the diagnosis and treatment of pulmonary nodules, willingness to participate in decision-making); (2) Ability (patient's lack of knowledge, physician's experience); and (3) Different worldview (misconception, distress among patients, preference for diagnosis and treatment).

CONCLUSIONS

Uncertainty in the management of pulmonary nodules is the opportunity to implement SDM. Patients' lack of knowledge, distress, and misunderstandings between healthcare providers and patients are both the main obstacles and the causes of the application of SDM.

Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT

Background

The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures.

Methods

We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established.

Results

(1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0.

Conclusion

We analyzed the risk factors for the development of 2-3  cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.

LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules

BACKGROUND

Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images.

OBJECTIVE

In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules.

METHODS

We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included.

RESULTS

The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively.

CONCLUSION

We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.

Uncertainty-aware image classification on 3D CT lung

Early detection is crucial for lung cancer to prolong the patient's survival. Existing model architectures used in such systems have shown promising results. However, they lack reliability and robustness in their predictions and the models are typically evaluated on a single dataset, making them overconfident when a new class is present. With the existence of uncertainty, uncertain images can be referred to medical experts for a second opinion. Thus, we propose an uncertainty-aware framework that includes three phases: data preprocessing and model selection and evaluation, uncertainty quantification (UQ), and uncertainty measurement and data referral for the classification of benign and malignant nodules using 3D CT images. To quantify the uncertainty, we employed three approaches; Monte Carlo Dropout (MCD), Deep Ensemble (DE), and Ensemble Monte Carlo Dropout (EMCD). We evaluated eight different deep learning models consisting of ResNet, DenseNet, and the Inception network family, all of which achieved average F1 scores above 0.832, and the highest average value of 0.845 was obtained using InceptionResNetV2. Furthermore, incorporating the UQ demonstrated significant improvement in the overall model performance. Upon evaluation of the uncertainty estimate, MCD outperforms the other UQ models except for the metric, URecall, where DE and EMCD excel, implying that they are better at identifying incorrect predictions with higher uncertainty levels, which is vital in the medical field. Finally, we show that using a threshold for data referral can greatly improve the performance further, increasing the accuracy up to 0.959.

An Incidental Discovery of the Intrathoracic Accessory Liver Lobe in a 72-Year-Old Man: Case Report and Literature Review

Accessory liver lobe is a rare finding, with the most common cases being accessory liver tissue on the gallbladder wall separate from the orthotopic liver. As the incidence of the ectopic liver is low there are only several case reports in published literature that describe similar presentations. We report a case of intrathoracic liver lobe that was connected to the main liver by a thick pedicle. Due to benign presentation, the patient was discharged without any surgical intervention. This case highlights the importance of understanding anatomical variability of internal organs, understanding the risks of torsion and malignant transformation of the accessory liver tissue. The literature review provides an excellent overview of published case series and reports, and outlines current recommendations on imaging, diagnosis, and management.

New developments in the imaging of lung cancer

Radiological and nuclear medicine methods play a fundamental role in the diagnosis and staging of patients with lung cancer. Imaging is essential in the detection, characterisation, staging and follow-up of lung cancer. Due to the increasing evidence, low-dose chest computed tomography (CT) screening for the early detection of lung cancer is being introduced to the clinical routine in several countries. Radiomics and radiogenomics are emerging fields reliant on artificial intelligence to improve diagnosis and personalised risk stratification. Ultrasound- and CT-guided interventions are minimally invasive methods for the diagnosis and treatment of pulmonary malignancies. In this review, we put more emphasis on the new developments in the imaging of lung cancer.

Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime?

Pulmonary nodules are ubiquitously found on computed tomography (CT) imaging either incidentally or via lung cancer screening and require careful diagnostic evaluation and management to both diagnose malignancy when present and avoid unnecessary biopsy of benign lesions. To engage in this complex decision-making, clinicians must first risk stratify pulmonary nodules to determine what the best course of action should be. Recent developments in imaging technology, computer processing power, and artificial intelligence algorithms have yielded radiomics-based computer-aided diagnosis tools that use CT imaging data including features invisible to the naked human eye to predict pulmonary nodule malignancy risk and are designed to be used as a supplement to routine clinical risk assessment. These tools vary widely in their algorithm construction, internal and external validation populations, intended-use populations, and commercial availability. While several clinical validation studies have been published, robust clinical utility and clinical effectiveness data are not yet currently available. However, there is reason for optimism as ongoing and future studies aim to target this knowledge gap, in the hopes of improving the diagnostic process for patients with pulmonary nodules.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT

OBJECTIVES

The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores.

MATERIALS AND METHODS

The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task.

RESULTS

The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%.

CONCLUSION

Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.

TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk

BACKGROUND

There is a need for biomarkers that improve accuracy compared with current demographic risk indices to detect individuals at the highest lung cancer risk. Improved risk determination will enable more effective lung cancer screening and better stratification of lung nodules into high or low-risk category. We previously reported discovery of a biomarker for lung cancer risk characterized by increased prevalence of TP53 somatic mutations in airway epithelial cells (AEC). Here we present results from a validation study in an independent retrospective case-control cohort.

METHODS

Targeted next generation sequencing was used to identify mutations within three TP53 exons spanning 193 base pairs in AEC genomic DNA.

RESULTS

TP53 mutation prevalence was associated with cancer status (P < 0.001). The lung cancer detection receiver operator characteristic (ROC) area under the curve (AUC) for the TP53 biomarker was 0.845 (95% confidence limits 0.749-0.942). In contrast, TP53 mutation prevalence was not significantly associated with age or smoking pack-years. The combination of TP53 mutation prevalence with PLCOM2012 risk score had an ROC AUC of 0.916 (0.846-0.986) and this was significantly higher than that for either factor alone (P < 0.03).

CONCLUSIONS

These results support the validity of the TP53 mutation prevalence biomarker and justify taking additional steps to assess this biomarker in AEC specimens from a prospective cohort and in matched nasal brushing specimens as a potential non-invasive surrogate specimen.

Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study

A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8-30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. A total of 280 IC tested, and 278 control patients met eligibility and analysis criteria and 197 were in each group after PSM (IC and control groups). Patients in the IC group were 74% less likely to undergo an invasive procedure as compared to the control group (absolute difference 14%, p <0.001) indicating that for every 7 patients tested, one unnecessary invasive procedure was avoided. Invasive procedure reduction corresponded to a reduction in risk classification, with 71 patients (36%) in the IC group classified as low risk (pCA < 5%). The proportion of IC group patients with malignant PNs sent to surveillance were not statistically different than the control group, 7.5% vs 3.5% for the IC vs. control groups, respectively (absolute difference 3.91%, p 0.075). The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules. Trial registration: Clinical trial registration: ClinicalTrials.gov NCT03766958.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing

BACKGROUND

Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes.

OBJECTIVE

We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes.

METHODS

We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time.

RESULTS

Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes.

CONCLUSIONS

Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.

Case report: Colonic actinomycosis - A rare cause of a locally advanced colonic tumour

INTRODUCTION AND IMPORTANCE

Colon cancer is a common malignancy and is often encountered initially as locally advanced disease. However, there are many benign clinical entities that may masquerade as complicated colonic malignancy. Abdominal actinomycosis is one such rare mimic.

CASE PRESENTATION

A 48-year-old female presented with a progressively enlarging abdominal mass with skin involvement and clinical features of partial large bowel obstruction. Computed tomography (CT) revealed a mid-transverse colonic lesion at the centre of an inflammatory phlegmon. At laparotomy, the mass was found to be adherent to the anterior abdominal wall, gastrocolic omentum, and loops of jejunum. En block resection was performed with primary anastomosis. Final histology showed no evidence of malignancy, but mural abscesses containing pathognomonic sulphur granules and actinomyces species.

CLINICAL DISCUSSION

Abdominal actinomycosis, particularly of the colon, is rare and exceptionally so in immunocompetent patients. However, the clinical and radiographic presentation often mimics more common conditions such as colon cancer. Accordingly, surgical resection is typically radical to clear margins, and diagnosis is made only on final histopathology.

CONCLUSION

Colonic actinomycosis is an uncommon infection but the diagnosis should be considered particularly in colonic masses with anterior abdominal wall involvement. Oncologic resection remains the mainstay of treatment and the diagnosis commonly made retrospectively given the rarity of the condition.

Benign lesions with 68Ga-FAPI uptake: a retrospective study

OBJECTIVES

Although FAPI, as a pan-tumor tracer, shows high expression in the malignancy imaging, FAPI uptake is also seen in some benign lesions. The purpose of this study was to retrospectively analyze the characteristics of benign lesions with FAPI uptake on 68Ga-FAPI PET/CT imaging.

METHODS

The electronic medical and imaging records of patients undergoing 68Ga-FAPI PET/CT imaging in the Department of Nuclear Medicine of our hospital from March 2020 to March 2022 were retrospectively analyzed. Patients with benign lesions confirmed by histopathological analysis or long-term follow-up of FAPI-positive lesions were included in the study.

RESULTS

A total of 44 patients (i.e., 44 benign lesions) were included in this study, including 14 women and 30 men, ranging in age from 19 to 74 years. Benign lesions involved eight systems, including liver (n = 3), tail of pancreas (n = 3), stomach (n = 3), esophagus (n = 1), lung (n = 14), and mediastinum (n = 2), sinuses (n = 1), brain (n = 2), lymph nodes (n = 5), kidneys (n = 4), bones (n = 2), muscles (n = 1), thyroid (n = 1), parathyroid gland (n = 1), and breast (n = 1). The mean SUVmax (p = 0.471) and mean TBR (p = 0.830) of benign lesions in the eight systems were not significantly different.

CONCLUSION

Our studies have shown that in addition to malignant tumors, certain benign lesions also show uptake of FAPI, and it is necessary for doctors to distinguish these benign lesions from true malignant tumors.

ADVANCES IN KNOWLEDGE

Benign lesions may also show FAPI expression, which may make the differential diagnosis of benign and malignant lesions difficult and should be alerted by physicians.

Image synthesis with disentangled attributes for chest X-ray nodule augmentation and detection

Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR images. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the shape/size attributes desired by the detector. To address this issue, we introduce a novel lung nodule synthesis framework in this paper, which decomposes nodule attributes into three main aspects including the shape, the size, and the texture, respectively. A GAN-based Shape Generator firstly models nodule shapes by generating diverse shape masks. The following Size Modulation then enables quantitative control on the diameters of the generated nodule shapes in pixel-level granularity. A coarse-to-fine gated convolutional Texture Generator finally synthesizes visually plausible nodule textures conditioned on the modulated shape masks. Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task. Our experiments demonstrate the enhanced image quality, diversity, and controllability of the proposed lung nodule synthesis framework. We also validate the effectiveness of our data augmentation strategy on greatly improving nodule detection performance.

Impact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer

INTRODUCTION

Growth assessment for pulmonary nodules is an important diagnostic tool; however, the impact on prognosis due to time delay for follow-up diagnostic scans needs to be considered.

METHODS

Using the data between 2003 and 2019 from the International Early Lung Cancer Action Program, a prospective cohort study, we determined the size-specific, 10-year Kaplan-Meier lung cancer (LC) survival rates as surrogates for cure rates. We estimated the change in LC diameter after delays of 90, 180, and 365 days using three representative LC volume doubling times (VDTs) of 60 (fast), 120 (moderate), and 240 (slow). We then estimated the decrease in the LC cure rate resulting from time between computed tomography scans to assess for growth during the diagnostic workup.

RESULTS

Using a regression model of the 10-year LC survival rates on LC diameter, the estimated LC cure rate of a 4.0 mm LC with fast (60-d) VDT is 96.0% (95% confidence interval [CI]: 95.2%-96.7%) initially, but it would decrease to 94.3% (95% CI: 93.2%-95.0%), 92.0% (95% CI: 90.5%-93.4%), and 83.6%(95% CI: 80.6%-86.6%) after delays of 90, 180, and 365 days, respectively. A 20.0-mm LC with the same VDTs has a lower LC cure rate of 79.9% (95% CI: 76.2%-83.5%) initially and decreases more rapidly to 71.5% (95% CI: 66.4%-76.7%), 59.8% (95% CI: 52.4%-67.1%), and 17.9% (95% CI: 3.0%-32.8%) after the same delays of 90, 180, and 365 days, respectively.

CONCLUSIONS

Time between scans required to measure growth of lung nodules affects prognosis with the effect being greater for fast growing and larger cancers. Quantifying the extent of change in prognosis is required to understand efficiencies of different management protocols.

Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network

Early detection and diagnosis of brain tumors are crucial to taking adequate preventive measures, as with most cancers. On the other hand, artificial intelligence (AI) has grown exponentially, even in such complex environments as medicine. Here it's proposed a framework to explore state-of-the-art deep learning architectures for brain tumor classification and detection. An own development called Cross-Transformer is also included, which consists of three scalar products that combine self-care model keys, queries, and values. Initially, we focused on the classification of three types of tumors: glioma, meningioma, and pituitary. With the Figshare brain tumor dataset was trained the InceptionResNetV2, InceptionV3, DenseNet121, Xception, ResNet50V2, VGG19, and EfficientNetB7 networks. Over 97 % of classifications were accurate in this experiment, which provided a network's performance overview. Subsequently, we focused on tumor detection using the Brain MRI Images for Brain Tumor Detection and The Cancer Genome Atlas Low-Grade Glioma database. The development encompasses learning transfer, data augmentation, as well as image acquisition sequences; T1-weighted images (T1WI), T1-weighted post-gadolinium (T1-Gd), and Fluid-Attenuated Inversion Recovery (FLAIR). Based on the results, using learning transfer and data augmentation increased accuracy by up to 6 %, with a p-value below the significance level of 0.05. As well, the FLAIR sequence was the most efficient for detection. As an alternative, our proposed model proved to be the most effective in terms of training time, using approximately half the time of the second fastest network.

Derivation and validation of a prediction model for patients with lung nodules malignancy regardless of mediastinal/hilar lymphadenopathy

BACKGROUND

Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included.

METHODS

A single-center retrospective study was conducted. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit test was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn.

RESULTS

There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The aera under the curve (AUC) of the validation set was 0.91 (95% confidence interval [CI]: 0.85-0.98). In the validation set with mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI: 0.90-0.99). The goodness-of-fit test was 0.22.

CONCLUSIONS

We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy in clinical practice.

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

OBJECTIVE

Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs.

METHOD

We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model).

RESULTS

Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g.

, AUC

0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model.

CONCLUSIONS

Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance.

Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis

During clinical practice, radiologists often use attributes, e.g., morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning and a graph convolutional network branch performing more generic relational modeling and reasoning using a feature representation. Tight coupling between these two branches is achieved via a cross-network attention mechanism and the fusion of their classification results. We have successfully applied our hybrid reasoning algorithm to two challenging medical image diagnosis tasks. On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95.36% and an AUC of 96.54%. Our method also achieves a 3.24% accuracy improvement on an in-house chest X-ray image dataset for tuberculosis diagnosis. Our ablation study indicates that our hybrid algorithm achieves a much better generalization performance than a pure neural network architecture under very limited training data.

The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers

BACKGROUND

Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect.

METHODS

Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets.

RESULTS

Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 &gt; 0.97 for each) with fitted parameters closely aligned with professional society guidelines.

CONCLUSIONS

The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice.

IMPACT

We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.

Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT

Background Limited data are available regarding whether computer-aided diagnosis (CAD) improves assessment of malignancy risk in indeterminate pulmonary nodules (IPNs). Purpose To evaluate the effect of an artificial intelligence-based CAD tool on clinician IPN diagnostic performance and agreement for both malignancy risk categories and management recommendations. Materials and Methods This was a retrospective multireader multicase study performed in June and July 2020 on chest CT studies of IPNs. Readers used only CT imaging data and provided an estimate of malignancy risk and a management recommendation for each case without and with CAD. The effect of CAD on average reader diagnostic performance was assessed using the Obuchowski-Rockette and Dorfman-Berbaum-Metz method to calculate estimates of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Multirater Fleiss κ statistics were used to measure interobserver agreement for malignancy risk and management recommendations. Results A total of 300 chest CT scans of IPNs with maximal diameters of 5-30 mm (50.0% malignant) were reviewed by 12 readers (six radiologists, six pulmonologists) (patient median age, 65 years; IQR, 59-71 years; 164 [55%] men). Readers' average AUC improved from 0.82 to 0.89 with CAD (P < .001). At malignancy risk thresholds of 5% and 65%, use of CAD improved average sensitivity from 94.1% to 97.9% (P = .01) and from 52.6% to 63.1% (P < .001), respectively. Average reader specificity improved from 37.4% to 42.3% (P = .03) and from 87.3% to 89.9% (P = .05), respectively. Reader interobserver agreement improved with CAD for both the less than 5% (Fleiss κ, 0.50 vs 0.71; P < .001) and more than 65% (Fleiss κ, 0.54 vs 0.71; P < .001) malignancy risk categories. Overall reader interobserver agreement for management recommendation categories (no action, CT surveillance, diagnostic procedure) also improved with CAD (Fleiss κ, 0.44 vs 0.52; P = .001). Conclusion Use of computer-aided diagnosis improved estimation of indeterminate pulmonary nodule malignancy risk on chest CT scans and improved interobserver agreement for both risk stratification and management recommendations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.

Diagnostic value of platelet-to-lymphocyte ratio in patients with solitary pulmonary nodules

Introduction

Nodules detected in the lung parenchyma should be considered as malignant until proven otherwise, and the necessary tests should be performed for diagnosis.

Aim

To calculate the preoperative platelet-to-lymphocyte ratio (PLR) in patients with malignant lung nodules and to investigate the diagnostic value of this ratio in determining the histopathology of the nodule.

Material and methods

Ninety-one patients who were operated on for a malignant nodule in the lung between September 2010 and September 2020 were included in the study. The PLR was calculated by dividing the absolute platelet count by the absolute lymphocyte count. These values were compared with the histopathological diagnoses of the resected tumor tissue. Patients with primary lung malignancy were classified as group 1 (n = 54), and lung metastases of other organs were classified as group 2 (n = 37).

Results

The mean PLR was 127.27 ±46.82 in the first group and 183.56 ±93.49 in the second group. There was a statistically significant difference in PLR values between the two groups, and PLR was higher in group 2. There was no statistically significant difference between the two groups in terms of lymph node positivity, nodule size and SuvMax values. A moderately strong, significant and same-sided correlation was observed between nodule size and SuvMax values in the first group of patients (r = 0.48, p = 0.001)

Conclusions

PLR values less than 89.41 indicate that the histopathological result may be a lung-derived malignancy. However, in cases where the PLR is detected above 165.6, it would be appropriate to interpret another previously detected malignancy as metastasis to the lung.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers

BACKGROUND

Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.

METHODS

Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.

RESULTS

Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.

CONCLUSIONS

A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

The Chain of Adherence for Incidentally Detected Pulmonary Nodules after an Initial Radiologic Imaging Study: A Multisystem Observational Study

Rationale: Millions of people are diagnosed with incidental pulmonary nodules every year. Although most nodules are benign, it is universally recommended that all patients be assessed to determine appropriate follow-up and ensure that it is obtained. Objectives: To determine the degree of concordance and adherence to 2005 Fleischner Society guidelines among radiologists, clinicians, and patients at two Veterans Affairs healthcare systems with incidental nodule tracking systems. Methods: Trained researchers abstracted data from the electronic health records of patients with incidental pulmonary nodules as identified by interpreting radiologists from 2008 to 2016. We classified radiology reports and patient follow-up into three categories. Radiologist-Fleischner adherence was the agreement between the radiologist's recommendation in the computed tomography (CT) report and the 2005 Fleischner Society guidelines. Clinician/patient-Fleischner concordance was agreement between patient follow-up and the guidelines. Clinician/patient-radiologist adherence was agreement between the radiologist's recommendation and patient follow-up. We evaluated whether the recommendation or follow-up was more (e.g., sooner) or less (e.g., later) aggressive than recommended. Results: After exclusions, 4,586 patients with 7,408 imaging tests (n = 4,586 initial chest CT scans; n = 2,717 follow-up chest CT scans; n = 105 follow-up low-dose CT scans) were included. Among radiology reports that could be classified in terms of Fleischner Society guidelines (n = 3,150), 80% had nonmissing radiologist recommendations. Among those reports, radiologist-Fleischner adherence was 86.6%, with 4.8% more aggressive and 8.6% less aggressive. Among patients whose initial scans could be classified, clinician/patient-Fleischner concordance was 46.0%, 14.5% were more aggressive, and 39.5% were less aggressive. Clinician/patient-radiologist adherence was 54.3%. Veterans whose radiology reports were adherent to Fleischner Society guidelines had a substantially higher proportion of clinician/patient-Fleischner concordance: 52.0% concordance among radiologist-Fleischner adherent versus 11.6% concordance among radiologist-Fleischner nonadherent. Conclusions: In this multi-health system observational study of incidental pulmonary nodule follow-up, we found that radiologist adherence to 2005 Fleischner Society guidelines may be necessary but not sufficient. Our results highlight the many facets of care processes that must occur to achieve guideline-concordant care.

Predicting malignant pleural effusion during diagnostic pleuroscopy with biopsy: A prospective multicentre study

BACKGROUND AND OBJECTIVE

Pleuroscopy with pleural biopsy has a high sensitivity for malignant pleural effusion (MPE). Because MPEs tend to recur, concurrent diagnosis and treatment of MPE during pleuroscopy is desired. However, proceeding directly to treatment at the time of pleuroscopy requires confidence in the on-site diagnosis. The study's primary objective was to create a predictive model to estimate the probability of MPE during pleuroscopy.

METHODS

A prospective observational multicentre cohort study of consecutive patients undergoing pleuroscopy was conducted. We used a logistic regression model to evaluate the probability of MPE with relation to visual assessment, rapid on-site evaluation (ROSE) of touch preparation and presence of pleural nodules/masses on computed tomography (CT). To assess the model's prediction accuracy, a bootstrapped training/testing approach was utilized to estimate the cross-validated area under the receiver operating characteristic curve.

RESULTS

Of the 201 patients included in the study, 103 had MPE. Logistic regression showed that higher level of malignancy on visual assessment is associated with higher odds of MPE (OR = 34.68, 95% CI = 9.17-131.14, p < 0.001). The logistic regression also showed that higher level of malignancy on ROSE of touch preparation is associated with higher odds of MPE (OR = 11.63, 95% CI = 3.85-35.16, p < 0.001). Presence of pleural nodules/masses on CT is associated with higher odds of MPE (OR = 6.61, 95% CI = 1.97-22.1, p = 0.002). A multivariable logistic regression model of final pathologic status with relation to visual assessment, ROSE of touch preparation and presence of pleural nodules/masses on CT had a cross-validated AUC of 0.94 (95% CI = 0.91-0.97).

CONCLUSION

A prediction model using visual assessment, ROSE of touch preparation and CT scan findings demonstrated excellent predictive accuracy for MPE. Further validation studies are needed to confirm our findings.

Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images

Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.

Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study

BACKGROUND

Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to "very high risk" with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence.

METHODS

Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision.

RESULTS

One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists' confidence in decision-making following a nondiagnostic bronchoscopy.

CONCLUSIONS

Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.

Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging

Background

Early identification of the malignant propensity of pulmonary ground‐glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning‐based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs.

Methods

This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time‐point CT scans. We developed a deep learning‐based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models.

Results

The deep learning model that used integrated DL‐features from initial and follow‐up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component.

Conclusions

Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.

A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels

OBJECTIVE

To establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels.

METHODS

Totally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Differences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = -5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) -0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763-0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification.

CONCLUSIONS

The prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs.

Are Incidental Minute Pulmonary Nodules Ultimately Determined to Be Metastatic Nodules in Esophageal Cancer Patients?

PURPOSE

Esophageal cancer patients may simultaneously have resectable esophageal cancer and undiagnosable incidental minute solid pulmonary nodules. While the latter is rarely metastatic, only a few studies have reported on the outcomes of such nodules after surgery. In this retrospective study, we assessed the incidence of such nodules, the probability that they are ultimately metastatic nodules, and the prognosis of patients after esophagectomy according to the metastatic status of the nodules.

METHODS

Data of 398 patients who underwent esophagectomy for resectable esophageal cancer between January 2012 and December 2016 were collected. We reviewed computed tomography (CT) images from the first visit and searched for incidental minute pulmonary nodules <10 mm in size. We followed the outcomes of these nodules and compared the characteristics of metastatic and nonmetastatic nodules. We also assessed the prognosis of patients whose minute pulmonary nodules were metastatic.

RESULTS

Among the patients who underwent esophagectomy, 149 (37.4%) had one or more minute pulmonary nodules, with a total of 285 nodules. Thirteen (4.6%) of these nodules in 12 (8.1%) patients were ultimately diagnosed as being metastatic. Thirteen (8.7%) patients experienced recurrence at a different location from where the nodules were originally identified. Characteristics of the metastatic nodules were not unique in terms of size, SUVmax, or location in the lungs. Two-year and 5-year overall survival rates of patients whose nodules were metastatic were 64.2 and 32.1%, respectively.

CONCLUSION

The rate of minute pulmonary nodules which were ultimately metastatic was 4.6%. Our findings suggest that esophagectomy followed by the identification of minute pulmonary nodules is an acceptable strategy even if the nodules cannot be diagnosed as being metastatic on the first visit CT due to their small size.

Clinical Implications of "Atypia" on Biopsy: Possible Precursor to Lung Cancer?

Background: It is common for biopsies of concerning pulmonary nodules to result in cytologic “atypia” on biopsy, which may represent a benign response or a false negative finding. This investigation evaluated time to diagnosis and factors which may predict an ultimate diagnosis of lung cancer in these patients with atypia cytology on lung nodule biopsy. Methods: This retrospective study included patients of the Stony Brook Lung Cancer Evaluation Center who had a biopsy baseline diagnosis of atypia between 2010 and 2020 and were either diagnosed with cancer or remained disease free by the end of the observation period. Cox Proportional Hazard (CPH) Models were used to assess factor effects on outcomes. Results: Among 106 patients with an initial diagnosis of atypia, 80 (75%) were diagnosed with lung cancer. Of those, over three-quarters were diagnosed within 6 months. The CPH models indicated that PET positivity (SUV ≥ 2.5) (HR = 1.74 (1.03, 2.94)), nodule size > 3.5 cm (HR = 2.83, 95% CI (1.47, 5.45)) and the presence of mixed ground glass opacities (HR = 2.15 (1.05, 4.43)) significantly increased risk of lung cancer. Conclusion: Given the high conversion rate to cancer within 6 months, at least tight monitoring, if not repeat biopsy may be warranted during this time period for patients diagnosed with atypia.

Managing screening-detected subsolid nodules-the Asian perspective

The broad application of low-dose computed tomography (CT) screening has resulted in the detection of many small pulmonary nodules. In Asia, a large number of these detected nodules with a radiological ground glass pattern are reported as lung adenocarcinomas or premalignant lesions, especially among female non-smokers. In this review article, we discuss controversial issues and conditions involving these subsolid pulmonary nodules that we often face in Asia, including a lack or insufficiency of current guidelines; the roles of preoperative biopsy and imaging; the location of lesions; appropriate selection of localization techniques; the roles of dissection and sampling of frozen sections and lymph nodes; multifocal lesions; and the roles of non-surgical treatment modalities. For these complex issues, we have tried to present up-to-date evidence and our own opinions regarding the management of subsolid nodules. It is our hope that this article helps surgeons and physicians to manage the complex issues involving ground glass nodules (GGNs) in a balanced manner in their daily practice and provokes further discussion towards better guidelines and/or algorithms.