Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

BACKGROUND: Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content. Denoising methods are needed to increase image quality. PURPOSE: The purpose of this study was to investigate the performance, limitations, and behavior of a new 4D filtering method, called the 4D Similarity Filter (4DSF), to reduce image noise in temporal CT data. METHODS: The 4DSF averages voxels with similar time-intensity curves (TICs). Each phase is filtered individually using the information of all phases except for the one being filtered. This approach minimizes the bias toward the noise initially present in this phase. Since the 4DSF does not base similarity on spatial proximity, loss of spatial resolution is avoided. The 4DSF was evaluated on a 12-phase liver dynamic CT angiography acquisition of 52 digital anthropomorphic phantoms, each containing one hypervascular 1 cm lesion with a small necrotic core. The metrics used for evaluation were noise reduction, lesion contrast-to-noise ratio (CNR), CT number accuracy using peak-time and peak-intensity of the TICs, and resolution loss. The results were compared to those obtained by the time-intensity profile similarity (TIPS) filter, which uses the whole TIC for determining similarity, and the combination 4DSF followed by TIPS filter (4DSF + TIPS). RESULTS: The 4DSF alone resulted in a median noise reduction by a factor of 6.8, which is lower than that obtained by the TIPS filter at 8.1, and 4DSF + TIPS at 12.2. The 4DSF increased the median CNR from 0. 44 to 1.85, which is less than the TIPS filter at 2.59 and 4DSF + TIPS at 3.12. However, the peak-intensity accuracy in the TICs was superior for the 4DSF, with a median intensity decrease of -34 HU compared to -88 and -50 HU for the hepatic artery when using the TIPS filter and 4DSF + TIPS, respectively. The median peak-time accuracy was inferior for the 4DSF filter and 4DSF + TIPS, with a time shift of -1 phases for the portal vein TIC compared to no shift in time when using the TIPS. The analysis of the full-width-at-half-maximum (FWHM) of a small artery showed significantly less spatial resolution loss for the 4DSF at 3.2 pixels, compared to the TIPS filter at 4.3 pixels, and 3.4 pixels for the 4DSF + TIPS. CONCLUSION: The 4DSF can reduce noise with almost no resolution loss, making the filter very suitable for denoising 4D CT data for detection tasks, even in very low dose, i.e., very high noise level, situations. In combination with the TIPS filter, the noise reduction can be increased even further.

Cost-effectiveness analysis of a lung cancer screening program in the netherlands: a simulation based on NELSON and NLST study outcomes.

BACKGROUND: In the Netherlands, lung cancer is the leading cause of cancer-related death, accounting for more than 10,000 annual deaths. Lung cancer screening (LCS) studies using low-dose computed tomography (LDCT) have demonstrated that early detection reduces lung cancer mortality. However, no LCS program has been implemented yet in the Netherlands. A national LCS program has the potential to enhance the health outcomes for lung cancer patients in the Netherlands. OBJECTIVE AND METHODS: This study evaluates the cost-effectiveness of LCS compared to no-screening in the Netherlands, by simulating the screening outcomes based on data from NEderlands-Leuvens Longkanker Screenings ONderzoek (NELSON) and National Lung Screening Trial (NLST). We simulated annual screening up to 74 years of age, using inclusion criteria from the respective studies. A decision tree and Markov model was used to predict the incremental costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICERs) for the screening population. The analysis used a lifetime horizon and a societal perspective. RESULTS: Compared to no-screening, LCS resulted in an ICER of €5,169 per QALY for the NELSON simulation, and an ICER of €17,119 per QALY for the NLST simulation. The screening costs were highly impactful for the cost-effectiveness. The most influential parameter was the CT scan cost. Cost reduction for CT from €201 to €101 per scan would reduce the ICER to €2,335 using NELSON criteria. Additionally, LCS could prevent 15,115 and 12,611 premature lung cancer deaths, accompanied by 1.66 and 1.31 QALYs gained per lung cancer case for the NELSON and NLST simulations, respectively. CONCLUSION: LCS was estimated to be cost-effective in the Netherlands for both simulations at a willingness-to-pay threshold of €20,000 per QALY. Using the NELSON criteria, less than €5,500 per QALY had to be spent. Lowering the cost per CT exam would lead to a further reduction of this amount.

Clinical applicability of signal heterogeneity and tumor border assessment on T2-weighted MR images to distinguish astrocytic from oligodendroglial origin of gliomas.

BACKGROUND AND PURPOSE: Radiological features on magnetic resonance imaging (MRI) were attributed to oligodendroglioma, although the diagnostic accuracy in a real-world clinical setting remains partially elusive. This study investigated the accuracy and robustness of tumor heterogeneity and tumor border delineation on T2-weighted MRI to distinguish oligodendroglioma from astrocytoma. MATERIALS AND METHODS: Eight readers from three different specialties (radiology, neurology, neurosurgery) with varying levels of experience blindly rated 79 T2-weighted MR images of patients with either oligodendroglioma or astrocytoma. After the first reading session, all readers were re-invited for a second reading session within three weeks. Diagnostic accuracy, including area under the receiver operator characteristics curve (AUC), and intra-observer variability and inter-observer variability were used as outcome measures. RESULTS: Pooled sensitivity and specificity to distinguish oligodendroglioma from astrocytoma for the use of tumor heterogeneity were 59.9 % respectively 74.5 %, and 85.7 % respectively 40.1 % for tumor border. A second reading session did not result in a significant change in sensitivity or specificity for tumor heterogeneity (P = 0.752 and P = 0.733, respectively) or tumor border (P = 0.309 and P = 0.271, respectively). An AUC of 0.825 was achieved with regard to predicting oligodendroglial origin of gliomas. Intra-observer agreement ranged from moderate to very good for tumor heterogeneity (kappa-value 0.43-0.87) and tumor border (0.40-0.84). A moderate inter-oberserver agreement was achieved for tumor heterogeneity and tumor border (kappa-value of 0.50 and 0.45, respectively). CONCLUSION: This study demonstrates that tumor heterogeneity and tumor borders on T2-weighted MRI could be used with moderate Finter-observer agreement to non-invasively distinguish oligodendroglioma from astrocytoma.

Ferumoxtran-10-enhanced MRI for pre-operative metastatic lymph node detection in pancreatic, duodenal, or periampullary adenocarcinoma.

OBJECTIVES: To assess 3-Tesla (3-T) ultra-small superparamagnetic iron oxide (USPIO)-enhanced MRI in detecting lymph node (LN) metastases for resectable adenocarcinomas of the pancreas, duodenum, or periampullary region in a node-to-node validation against histopathology. METHODS: Twenty-seven consecutive patients with a resectable pancreatic, duodenal, or periampullary adenocarcinoma were enrolled in this prospective single expert centre study. Ferumoxtran-10-enhanced 3-T MRI was performed pre-surgery. LNs found on MRI were scored for suspicion of metastasis by two expert radiologists using a dedicated scoring system. Node-to-node matching from in vivo MRI to histopathology was performed using a post-operative ex vivo 7-T MRI of the resection specimen. Sensitivity and specificity were calculated using crosstabs. RESULTS: Eighteen out of 27 patients (median age 65 years, 11 men) were included in the final analysis (pre-surgery withdrawal n = 4, not resected because of unexpected metastases peroperatively n = 2, and excluded because of inadequate contrast-agent uptake n = 3). On MRI 453 LNs with a median size of 4.0 mm were detected, of which 58 (13%) were classified as suspicious. At histopathology 385 LNs with a median size of 5.0 mm were found, of which 45 (12%) were metastatic. For 55 LNs node-to-node matching was possible. Analysis of these 55 matched LNs, resulted in a sensitivity and specificity of 83% (95% CI: 36-100%) and 92% (95% CI: 80-98%), respectively. CONCLUSION: USPIO-enhanced MRI is a promising technique to preoperatively detect and localise LN metastases in patients with pancreatic, duodenal, or periampullary adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Detection of (distant) LN metastases with USPIO-enhanced MRI could be used to determine a personalised treatment strategy that could involve neoadjuvant or palliative chemotherapy, guided resection of distant LNs, or targeted radiotherapy. REGISTRATION: The study was registered on clinicaltrials.gov NCT04311047. https://clinicaltrials.gov/ct2/show/NCT04311047?term=lymph+node&cond=Pancreatic+Cancer&cntry=NL&draw=2&rank=1 . KEY POINTS: LN metastases of pancreatic, duodenal, or periampullary adenocarcinoma cannot be reliably detected with current imaging. This technique detected LN metastases with a sensitivity and specificity of 83% and 92%, respectively. MRI with ferumoxtran-10 is a promising technique to improve preoperative staging in these cancers.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation.

OBJECTIVE: To investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules. METHODS AND MATERIALS: In this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty. RESULTS: The DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group. CONCLUSION: The integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance. CLINICAL RELEVANCE STATEMENT: Deep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction. KEY POINTS: • Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm. • Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse. • Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.

Optimized, Person-Centered Workflow Design for a High-Throughput Breast MRI Screening Facility-A Simulation Study.

OBJECTIVES: This project aims to model an optimal scanning environment for breast magnetic resonance imaging (MRI) screening based on real-life data to identify to what extent the logistics of breast MRI can be optimized. MATERIALS AND METHODS: A novel concept for a breast MRI screening facility was developed considering layout of the building, workflow steps, used resources, and MRI protocols. The envisioned screening facility is person centered and aims for an efficient workflow-oriented design. Real-life data, collected from existing breast MRI screening workflows, during 62 scans in 3 different hospitals, were imported into a 3D simulation software for designing and testing new concepts. The model provided several realistic, virtual, logistical pathways for MRI screening and their outcome measures: throughput, waiting times, and other relevant variables. RESULTS: The total average appointment time in the baseline scenario was 25:54 minutes, with 19:06 minutes of MRI room occupation. Simulated improvements consisted of optimizing processes and resources, facility layout, and scanning protocol. In the simulation, time spent in the MRI room was reduced by introducing an optimized facility layout, dockable tables, and adoption of an abbreviated MRI scanning protocol. The total average appointment time was reduced to 19:36 minutes, and in this scenario, the MRI room was occupied for 06:21 minutes. In the most promising scenario, screening of about 68 people per day (10 hours) on a single MRI scanner could be feasible, compared with 36 people per day in the baseline scenario. CONCLUSIONS: This study suggests that by optimizing workflow MRI for breast screening total appointment duration and MRI occupation can be reduced. A throughput of up to 6 people per hour may be achieved, compared with 3 people per hour in the current setup.

The Facilitators and Barriers of the Implementation of a Clinical Decision Support System for Breast Cancer Multidisciplinary Team Meetings-An Interview Study.

BACKGROUND: AI-driven clinical decision support systems (CDSSs) hold promise for multidisciplinary team meetings (MDTMs). This study aimed to uncover the hurdles and aids in implementing CDSSs during breast cancer MDTMs. METHODS: Twenty-four core team members from three hospitals engaged in semi-structured interviews, revealing a collective interest in experiencing CDSS workflows in clinical practice. All interviews were audio recorded, transcribed verbatim and analyzed anonymously. A standardized approach, 'the framework method', was used to create an analytical framework for data analysis, which was performed by two independent researchers. RESULTS: Positive aspects included improved data visualization, time-saving features, automated trial matching, and enhanced documentation transparency. However, challenges emerged, primarily concerning data connectivity, guideline updates, the accuracy of AI-driven suggestions, and the risk of losing human involvement in decision making. Despite the complexities involved in CDSS development and integration, clinicians demonstrated enthusiasm to explore its potential benefits. CONCLUSIONS: Acknowledging the multifaceted nature of this challenge, insights into the barriers and facilitators identified in this study offer a potential roadmap for smoother future implementations. Understanding these factors could pave the way for more effective utilization of CDSSs in breast cancer MDTMs, enhancing patient care through informed decision making.

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules.

BACKGROUND: During lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability. METHODS: In total, 724 IPNs (size 50-500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP). RESULTS: The random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods. CONCLUSION: ML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening.

OBJECTIVE: An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening. METHODS: In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD. RESULTS: Mean reading time was 162 (95% CI: 111-212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47-107) and 104 (95% CI: 71-136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33-41 s for DL-CAD as second reader. This translates into €1.0-4.3 per-case cost for concurrent reading and €0.8-5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300-53,600 for concurrent reader, and 9400-65,000 for pre-screening reader in the three countries. CONCLUSIONS: Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving. CRITICAL RELEVANCE STATEMENT: Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential. KEY POINTS: • DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even. • DL-CAD must be used in a high-workload screening environment to achieve break-even. • DL-CAD as a pre-screening reader shows the largest potential to be cost-saving.

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Background: Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. Methods: We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Results: Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Conclusions: Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans.

BACKGROUND: Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting. METHOD: We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists. RESULTS: On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6). CONCLUSIONS: The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.

Early-stage lung cancer can be diagnosed after identifying an abnormal spot on a chest CT scan ordered for other medical reasons. These spots or lung nodules can be overlooked by radiologists, as they are not necessarily the focus of an examination and can be as small as a few millimeters. Software using Artificial Intelligence (AI) technology has proven to be successful for aiding radiologists in this task, but its performance is understudied outside a lung cancer screening setting. We therefore developed and validated AI software for the detection of cancerous nodules or non-cancerous nodules that would need attention. We show that the software can reliably detect these nodules in a non-screening setting and could potentially aid radiologists in daily clinical practice.

ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

BACKGROUND: Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS: A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS: Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS: This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.

Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head.

The preoperative prediction of resectability pancreatic ductal adenocarcinoma (PDAC) is challenging. This retrospective single-center study examined tumor and vessel radiomics to predict the resectability of PDAC in chemo-naïve patients. The tumor and adjacent arteries and veins were segmented in the portal-venous phase of contrast-enhanced CT scans, and radiomic features were extracted. Features were selected via stability and collinearity testing, and least absolute shrinkage and selection operator application (LASSO). Three models, using tumor features, vessel features, and a combination of both, were trained with the training set (N = 86) to predict resectability. The results were validated with the test set (N = 15) and compared to the multidisciplinary team's (MDT) performance. The vessel-features-only model performed best, with an AUC of 0.92 and sensitivity and specificity of 97% and 73%, respectively. Test set validation showed a sensitivity and specificity of 100% and 88%, respectively. The combined model was as good as the vessel model (AUC = 0.91), whereas the tumor model showed poor performance (AUC = 0.76). The MDT's prediction reached a sensitivity and specificity of 97% and 84% for the training set and 88% and 100% for the test set, respectively. Our clinician-independent vessel-based radiomics model can aid in predicting resectability and shows performance comparable to that of the MDT. With these encouraging results, improved, automated, and generalizable models can be developed that reduce workload and can be applied in non-expert hospitals.

ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer.

BACKGROUND: Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS: A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS: Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS: This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.

Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.

Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; P = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; P = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; P < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; P < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT02837809 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Horst and Nishino in this issue.

Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals.

OBJECTIVE: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. METHODS: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. RESULTS: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. CONCLUSION: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. CLINICAL RELEVANCE STATEMENT: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. KEY POINTS: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.

Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial.

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15-20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40-50%.

Impact of time-to-treatment on survival for advanced non-small cell lung cancer patients in the Netherlands: a nationwide observational cohort study.

BACKGROUND: The assumption that more rapid treatment improves survival of advanced non-small cell lung cancer (NSCLC) has not yet been proven. We studied the relation between time-to-treatment and survival in advanced stage NSCLC patients in a large multicentric nationwide retrospective cohort. Additionally, we identified factors associated with delay. METHOD: We selected 10 306 patients, diagnosed and treated between 2014 and 2019 for clinical stage III and IV NSCLC, from the Netherlands Cancer Registry that includes nationwide data from 109 Dutch hospitals. Associations between survival and time-to-treatment were tested with Cox proportional hazard regression analyses. Time-to-treatment was adjusted for multiple covariates including diagnostic procedures and type of therapy. Factors associated with delay were identified by multilevel logistic regression. RESULTS: Risk of death significantly decreased with longer time-to-treatment for stage III patients receiving only radiotherapy (adjusted HR, aHR >21 days: 0.59 (95% CI 0.48 to 0.73)) or any type of systemic therapy (aHR >49 days: 0.72 (95% CI 0.56 to 0.91)) and stage IV patients receiving chemotherapy and/or immunotherapy (aHR >21 days: 0.81 (95% CI 0.73 to 0.88)). No significant association was found for stage III patients treated with chemoradiotherapy and stage IV patients treated with targeted therapy. More complex diagnostic procedures often delay treatment. CONCLUSION: Although in general it is important to start treatment as early as possible, our study finds no evidence that a more rapid start of treatment improves outcomes in advanced stage NSCLC patients. The benefit of urgent treatment is probably confounded by unmeasured patient and tumour characteristics and, clinical urgency dictating timelines of treatment. Time-to-treatment and its impact should be continuously evaluated as therapeutic strategies continue to evolve and improve.

Mapping Current Organizational Structure and Improvement Points of Breast Cancer Multidisciplinary Team Meetings - An Interview Study.

Purpose: The aim of the study was to map current organization, and document potential improvement points of breast cancer multidisciplinary team meetings (MDTMs), in order to support the optimization of the present breast cancer MDTM organization. Methods: From January 2019 to February 2021, 24 core team members of the breast cancer multidisciplinary team (MDT) in three hospitals were interviewed. Semi-structured interviews were performed based on an interview guide. All interviews were recorded and transcribed verbatim. Deductive coding was performed on the transcripts by two independent researchers. The codes were organized in categories and themes. Results: In total 24 healthcare professionals; surgeons, medical oncologists, radiotherapists, pathologists, radiologists, and specialized nurses, from three different hospitals were interviewed. According to the participants, improving efficiency before and during MDTMs is possible by ensuring proper preparation of attendees, implementing more structure during discussions, improving access to and availability of patient data and optimizing general meeting discipline. Conclusion: Preparation, structure, data availability and meeting discipline were highlighted as essential factors for efficient breast cancer MDTM improvement. These topics seem to be applicable to other types of oncology MDTMs as well. Improving MDTM efficiency on the long term ensures high-quality discussions for all breast cancer patients.