Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement.

Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer. Keywords: CT, Thorax, Lung, Computer Applications-Detection/Diagnosis, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management.

OBJECTIVES: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. METHODS: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen's kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. RESULTS: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58-0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. CONCLUSIONS: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. KEY POINTS: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs.

The Effect of Supplementary Bone-Suppressed Chest Radiographs on the Assessment of a Variety of Common Pulmonary Abnormalities: Results of an Observer Study.

PURPOSE: The aim of the study was to investigate the effect of bone-suppressed chest radiographs on the detection of common chest abnormalities. MATERIALS AND METHODS: A total of 261 posteroanterior and lateral chest radiographs were collected from 2 hospitals. Radiographs could contain single or multiple focal opacities <3 cm (n=66), single or multiple focal opacities >3 cm (n=33), diffuse lung disease (n=49), signs of cardiogenic congestion (n=26), or no abnormalities (n=110). Twenty-one cases contained >1 type of disease. All abnormalities were confirmed by a computed tomographic scan obtained within 4 weeks of the radiograph. Bone-suppressed images (BSIs) were generated from every posteroanterior radiograph (ClearRead BSI 3.2). All cases were read by 6 radiologists without BSI, followed by an evaluation of the same case with BSI. Presence or absence of each disease category and confidence (0-100) of the observers were documented for each interpretation. Differences in the number of correct detections without and with BSI were analyzed using the Wilcoxon signed-rank test. RESULTS: On average, 6 more cases with focal lesions were correctly identified with BSI (P=0.03), and 1 additional case with diffuse abnormalities was found with BSI (P=0.32). None of the observers demonstrated a decrease in the number of correctly detected cases with diffuse abnormalities or cardiogenic congestion with BSI. False positives in normal cases with availability of BSI mainly referred to the detection of small focal lesions (on average 7 per reader; P=0.04). CONCLUSIONS: BSI does not negatively affect the interpretation of diffuse lung disease, while improving visualization of focal lesions on chest radiographs. BSI leads to overcalling of focal abnormalities in normal radiographs.

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules?

Pulmonary subsolid nodules (SSNs) have a high likelihood of malignancy, but are often indolent. A conservative treatment approach may therefore be suitable. The aim of the current study was to evaluate whether close follow-up of SSNs with computed tomography may be a safe approach. The study population consisted of participants of the Dutch-Belgian lung cancer screening trial (Nederlands Leuvens Longkanker Screenings Onderzoek; NELSON). All SSNs detected during the trial were included in this analysis. Retrospectively, all persistent SSNs and SSNs that were resected after first detection were segmented using dedicated software, and maximum diameter, volume and mass were measured. Mass doubling time (MDT) was calculated. In total 7135 volunteers were included in the current analysis. 264 (3.3%) SSNs in 234 participants were detected during the trial. 147 (63%) of these SSNs in 126 participants disappeared at follow-up, leaving 117 persistent or directly resected SSNs in 108 (1.5%) participants available for analysis. The median follow-up time was 95 months (range 20-110 months). 33 (28%) SSNs were resected and 28 of those were (pre-) invasive. None of the non-resected SSNs progressed into a clinically relevant malignancy. Persistent SSNs rarely developed into clinically manifest malignancies unexpectedly. Close follow-up with computed tomography may be a safe option to monitor changes.

Semi-automatic quantification of subsolid pulmonary nodules: comparison with manual measurements.

RATIONALE: Accurate measurement of subsolid pulmonary nodules (SSN) is becoming increasingly important in the management of these nodules. SSNs were previously quantified with time-consuming manual measurements. The aim of the present study is to test the feasibility of semi-automatic SSNs measurements and to compare the results to the manual measurements. METHODS: In 33 lung cancer screening participants with 33 SSNs, the nodules were previously quantified by two observers manually. In the present study two observers quantified these nodules by using semi-automated nodule volumetry software. Nodules were quantified for effective diameter, volume and mass. The manual and semi-automatic measurements were compared using Bland-Altman plots and paired T tests. Observer agreement was calculated as an intraclass correlation coefficient. Data are presented as mean (SD). RESULTS: Semi-automated measurements were feasible in all 33 nodules. Nodule diameter, volume and mass were 11.2 (3.3) mm, 935 (691) ml and 379 (311) milligrams for observer 1 and 11.1 (3.7) mm, 986 (797) ml and 399 (344) milligrams for observer 2, respectively. Agreement between observers and within observer 1 for the semi-automatic measurements was good with an intraclass correlation coefficient >0.89. For observer 1 and observer 2, measured diameter was 8.8% and 10.3% larger (p<0.001), measured volume was 24.3% and 26.5% larger (p<0.001) and measured mass was 10.6% and 12.0% larger (p<0.001) with the semi-automatic program compared to the manual measurements. CONCLUSION: Semi-automated measurement of the diameter, volume and mass of SSNs is feasible with good observer agreement. Semi-automated measurement makes quantification of mass and volume feasible in daily practice.

Computed tomography pulmonary angiography in acute pulmonary embolism: the effect of a computer-assisted detection prototype used as a concurrent reader.

PURPOSE: To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. RESULTS: Using logistic regression for repeated measurements, we found a significant increase in readers' sensitivity (P<0.001) without loss of specificity (P=0.855) with the effects being reader dependent (P<0.001). Sensitivities varied from 68% to 100% without CAD and from 76% to 100% with CAD. A 2-way analysis of variance showed a small but significant decrease in reading time (P<0.001), with the duration varying between 24 and 208 seconds without CAD and between 17 and 196 seconds with CAD, and a significant increase in readers' confidence scores using CAD as a concurrent reader (P<0.001). CONCLUSIONS: CAD as a concurrent reader has the potential to increase readers' sensitivity and confidence with a decrease in reading time without loss of specificity. The differences between readers, however, require further evaluation of CAD as a concurrent reader in a larger trial before stronger conclusions can be drawn.

Pulmonary perifissural nodules on CT scans: rapid growth is not a predictor of malignancy.

PURPOSE: To assess the prevalence, natural course, and malignancy rate of perifissural nodules (PFNs) in smokers participating in a lung cancer screening trial. MATERIALS AND METHODS: As part of the ethics-committee approved Dutch-Belgian Randomised Lung Cancer Multi-Slice Screening Trial (NELSON), computed tomography (CT) was used to screen 2994 current or former heavy smokers, aged 50-74 years, for lung cancer. CT was repeated after 1 and 3 years, with additional follow-up CT scans if necessary. All baseline CT scans were screened for nodules. Nodule volume was determined with automated volumetric analysis. Homogeneous solid nodules, attached to a fissure with a lentiform or triangular shape, were classified as PFNs. Nodules were considered benign if they did not grow during the total follow-up period or were proved to be benign in a follow-up by a pulmonologist. Prevalence, growth, and malignancy rate of PFNs were assessed. RESULTS: At baseline screening, 4026 nodules were detected in 1729 participants, and 19.7% (794 of 4026) of the nodules were classified as PFNs. The mean size of the PFNs was 4.4 mm (range: 2.8-10.6 mm) and the mean volume was 43 mm3 (range: 13-405 mm3). None of the PFNs were found to be malignant during follow-up. Between baseline and the first follow-up CT scan, 15.5% (123 of 794) were found to have grown, and 8.3% (66 of 794) had a volume doubling time of less than 400 days. One PFN was resected and proved to be a lymph node. CONCLUSION: PFNs are frequently found at CT scans for lung cancer. They can show growth rates in the range of malignant nodules, but none of the PFNs in the present study turned out to be malignant. Recognition of PFNs can reduce the number of follow-up examinations required for the workup of suspicious nodules.

Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT.

PURPOSE: To analyze pulmonary function using a fully automatic technique which processes pairs of thoracic CT scans acquired at breath-hold inspiration and expiration, respectively. The following research objectives are identified to: (a) describe and systematically analyze the processing pipeline and its results; (b) verify that the quantitative, regional ventilation measurements acquired through CT are meaningful for pulmonary function analysis; (c) identify the most effective of the calculated measurements in predicting pulmonary function; and (d) demonstrate the potential of the system to deliver clinically important information not available through conventional spirometry. METHODS: A pipeline of automatic segmentation and registration techniques is presented and demonstrated on a database of 216 subjects well distributed over the various stages of COPD (chronic obstructive pulmonary disorder). Lungs, fissures, airways, lobes, and vessels are automatically segmented in both scans and the expiration scan is registered with the inspiration scan using a fully automatic nonrigid registration algorithm. Segmentations and registrations are examined and scored by expert observers to analyze the accuracy of the automatic methods. Quantitative measures representing ventilation are computed at every image voxel and analyzed to provide information about pulmonary function, both globally and on a regional basis. These CT derived measurements are correlated with results from spirometry tests and used as features in a kNN classifier to assign COPD global initiative for obstructive lung disease (GOLD) stage. RESULTS: The steps of anatomical segmentation (of lungs, lobes, and vessels) and registration in the workflow were shown to perform very well on an individual basis. All CT-derived measures were found to have good correlation with spirometry results, with several having correlation coefficients, r, in the range of 0.85-0.90. The best performing kNN classifier succeeded in classifying 67% of subjects into the correct COPD GOLD stage, with a further 29% assigned to a class neighboring the correct one. CONCLUSIONS: Pulmonary function information can be obtained from thoracic CT scans using the automatic pipeline described in this work. This preliminary demonstration of the system already highlights a number of points of clinical importance such as the fact that an inspiration scan alone is not optimal for predicting pulmonary function. It also permits measurement of ventilation on a per lobe basis which reveals, for example, that the condition of the lower lobes contributes most to the pulmonary function of the subject. It is expected that this type of regional analysis will be instrumental in advancing the understanding of multiple pulmonary diseases in the future.

CT-quantified emphysema in male heavy smokers: association with lung function decline.

BACKGROUND: Emphysema and small airway disease both contribute to chronic obstructive pulmonary disease (COPD), a disease characterised by accelerated decline in lung function. The association between the extent of emphysema in male current and former smokers and lung function decline was investigated. METHODS: Current and former heavy smokers participating in a lung cancer screening trial were recruited to the study and all underwent CT. Spirometry was performed at baseline and at 3-year follow-up. The 15th percentile (Perc15) was used to assess the severity of emphysema. RESULTS: 2085 men of mean age 59.8 years participated in the study. Mean (SD) baseline Perc15 was -934.9 (19.5) HU. A lower Perc15 value correlated with a lower forced expiratory volume in 1 s (FEV(1)) at baseline (r=0.12, p<0.001). Linear mixed model analysis showed that a lower Perc15 was significantly related to a greater decline in FEV(1) after follow-up (p<0.001). Participants without baseline airway obstruction who developed it after follow-up had significantly lower mean (SD) Perc15 values at baseline than those who did not develop obstruction (-934.2 (17.1) HU vs -930.2 (19.7) HU, p<0.001). CONCLUSION: Greater baseline severity of CT-detected emphysema is related to lower baseline lung function and greater rates of lung function decline, even in those without airway obstruction. CT-detected emphysema aids in identifying non-obstructed male smokers who will develop airflow obstruction.

Computer-aided detection of lung cancer on chest radiographs: effect on observer performance.

PURPOSE: To assess how computer-aided detection (CAD) affects reader performance in detecting early lung cancer on chest radiographs. MATERIALS AND METHODS: In this ethics committee-approved study, 46 individuals with 49 computed tomographically (CT)-detected and histologically proved lung cancers and 65 patients without nodules at CT were retrospectively included. All subjects participated in a lung cancer screening trial. Chest radiographs were obtained within 2 months after screening CT. Four radiology residents and two experienced radiologists were asked to identify and localize potential cancers on the chest radiographs, first without and subsequently with the use of CAD software. A figure of merit was calculated by using free-response receiver operating characteristic analysis. RESULTS: Tumor diameter ranged from 5.1 to 50.7 mm (median, 11.8 mm). Fifty-one percent (22 of 49) of lesions were subtle and detected by two or fewer readers. Stand-alone CAD sensitivity was 61%, with an average of 2.4 false-positive annotations per chest radiograph. Average sensitivity was 63% for radiologists at 0.23 false-positive annotations per chest radiograph and 49% for residents at 0.45 false-positive annotations per chest radiograph. Figure of merit did not change significantly for any of the observers after using CAD. CAD marked between five and 16 cancers that were initially missed by the readers. These correctly CAD-depicted lesions were rejected by radiologists in 92% of cases and by residents in 77% of cases. CONCLUSION: The sensitivity of CAD in identifying lung cancers depicted with CT screening was similar to that of experienced radiologists. However, CAD did not improve cancer detection because, especially for subtle lesions, observers were unable to sufficiently differentiate true-positive from false-positive annotations.

Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study.

Numerous publications and commercial systems are available that deal with automatic detection of pulmonary nodules in thoracic computed tomography scans, but a comparative study where many systems are applied to the same data set has not yet been performed. This paper introduces ANODE09 ( http://anode09.isi.uu.nl), a database of 55 scans from a lung cancer screening program and a web-based framework for objective evaluation of nodule detection algorithms. Any team can upload results to facilitate benchmarking. The performance of six algorithms for which results are available are compared; five from academic groups and one commercially available system. A method to combine the output of multiple systems is proposed. Results show a substantial performance difference between algorithms, and demonstrate that combining the output of algorithms leads to marked performance improvements.

Screening for lung cancer with digital chest radiography: sensitivity and number of secondary work-up CT examinations.

PURPOSE: To estimate the performance of digital chest radiography for detection of lung cancer. MATERIALS AND METHODS: The study had ethics committee approval, and a nested case-control design was used and included 55 patients with lung cancer detected at computed tomography (CT) and confirmed with histologic examination and a sample of 72 of 4873 control subjects without nodules at CT. All patients underwent direct-detector digital chest radiography in two projections within 2 months of the screening CT. Four radiologists with varying experience identified and localized potential cancers on chest radiographs by using a confidence scale of level 1 (no lesion) to 5 (definite lesion). Localization receiver operating characteristic (ROC) analysis was performed. On the basis of the assumption that suspicious lesions seen at chest radiography would lead to further work-up with CT, the number of work-up CT examinations per detected cancer (CT examinations per cancer) was calculated at various confidence levels for the screening population (cancer rate in study population, 1.3%). RESULTS: Tumor size ranged from 6.8 to 50.7 mm (median, 11.8 mm). Areas under the localization ROC curve ranged from 0.52 to 0.69. Detection rates substantially varied with the observers' experience and confidence level: At a confidence level of 5, detection rates ranged from 18% at one CT examination per cancer to 53% at 13 CT examinations per cancer. At a confidence level of 2 or higher, detection rates ranged from 94% at 62 CT examinations per cancer to 78% at 44 CT examinations per cancer. CONCLUSION: A detection rate of 94% for lung tumors with a diameter of 6.8-50.7 mm found at CT screening was achievable with chest radiography only at the expense of a high false-positive rate and an excessive number of work-up CT examinations. Detection performance is strongly observer dependent.

Automatic segmentation of pulmonary lobes robust against incomplete fissures.

A method for automatic segmentation of pulmonary lobes from computed tomography (CT) scans is presented that is robust against incomplete fissures. The method is based on a multiatlas approach in which existing lobar segmentations are deformed to test scans in which the fissures, the lungs, and the bronchial tree have been automatically segmented. The key element of our method is a cost function that exploits information from fissures, lung borders, and bronchial tree in an effective way, such that less reliable information (lungs, airways) is only used when the most reliable information (fissures) is missing. To cope with the anatomical variation in lobe shape, an atlas selection mechanism is introduced. The method is evaluated on two test sets of 120 scans in total. The results show that the lobe segmentation closely follows the fissures when they are present. In a simulated experiment in which parts of complete fissures are removed, the robustness of the method against different levels of incomplete fissures is shown. When the fissures are incomplete, an observer study shows agreement of the automatically determined lobe borders with a radiologist for 81% of the lobe borders on average.

Pulmonary ground-glass nodules: increase in mass as an early indicator of growth.

PURPOSE: To compare manual measurements of diameter, volume, and mass of pulmonary ground-glass nodules (GGNs) to establish which method is best for identifying malignant GGNs by determining change across time. MATERIALS AND METHODS: In this ethics committee-approved retrospective study, baseline and follow-up CT examinations of 52 GGNs detected in a lung cancer screening trial were included, resulting in 127 GGN data sets for evaluation. Two observers measured GGN diameter with electronic calipers, manually outlined GGNs to obtain volume and mass, and scored whether a solid component was present. Observer 1 repeated all measurements after 2 months. Coefficients of variation and limits of agreement were calculated by using Bland-Altman methods. In a subgroup of GGNs containing all resected malignant lesions, the ratio between intraobserver variability and growth (growth-to-variability ratio) was calculated for each measurement technique. In this subgroup, the mean time for growth to exceed the upper limit of agreement of each measurement technique was determined. RESULTS: The kappa values for intra- and interobserver agreement for identifying a solid component were 0.55 and 0.38, respectively. Intra- and interobserver coefficients of variation were smallest for GGN mass (P < .001). Thirteen malignant GGNs were resected. Mean growth-to-variability ratios were 11, 28, and 35 for diameter, volume, and mass, respectively (P = .03); mean times required for growth to exceed the upper limit of agreement were 715, 673, and 425 days, respectively (P = .02). CONCLUSION: Mass measurements can enable detection of growth of GGNs earlier and are subject to less variability than are volume or diameter measurements.

Management of lung nodules detected by volume CT scanning.

BACKGROUND: The use of multidetector computed tomography (CT) in lung-cancer screening trials involving subjects with an increased risk of lung cancer has highlighted the problem for the clinician of deciding on the best course of action when noncalcified pulmonary nodules are detected by CT. METHODS: A total of 7557 participants underwent CT screening in years 1, 2, and 4 of a randomized trial of lung-cancer screening. We used software to evaluate a noncalcified nodule according to its volume or volume-doubling time. Growth was defined as an increase in volume of at least 25% between two scans. The first-round screening test was considered to be negative if the volume of a nodule was less than 50 mm(3), if it was 50 to 500 mm(3) but had not grown by the time of the 3-month follow-up CT, or if, in the case of those that had grown, the volume-doubling time was 400 days or more. RESULTS: In the first and second rounds of screening, 2.6% and 1.8% of the participants, respectively, had a positive test result. In round one, the sensitivity of the screen was 94.6% (95% confidence interval [CI], 86.5 to 98.0) and the negative predictive value 99.9% (95% CI, 99.9 to 100.0). In the 7361 subjects with a negative screening result in round one, 20 lung cancers were detected after 2 years of follow-up. CONCLUSIONS: Among subjects at high risk for lung cancer who were screened in three rounds of CT scanning and in whom noncalcified pulmonary nodules were evaluated according to volume and volume-doubling time, the chances of finding lung cancer 1 and 2 years after a negative first-round test were 1 in 1000 and 3 in 1000, respectively. (Current Controlled Trials number, ISRCTN63545820.)

Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection.

Lung segmentation is a prerequisite for automated analysis of chest CT scans. Conventional lung segmentation methods rely on large attenuation differences between lung parenchyma and surrounding tissue. These methods fail in scans where dense abnormalities are present, which often occurs in clinical data. Some methods to handle these situations have been proposed, but they are too time consuming or too specialized to be used in clinical practice. In this article, a new hybrid lung segmentation method is presented that automatically detects failures of a conventional algorithm and, when needed, resorts to a more complex algorithm, which is expected to produce better results in abnormal cases. In a large quantitative evaluation on a database of 150 scans from different sources, the hybrid method is shown to perform substantially better than a conventional approach at a relatively low increase in computational cost.

Prostate cancer: detection of lymph node metastases outside the routine surgical area with ferumoxtran-10-enhanced MR imaging.

PURPOSE: To prospectively evaluate the feasibility of magnetic resonance (MR) imaging with ferumoxtran-10 in patients with prostate cancer to depict lymph node metastases outside the routine pelvic lymph node dissection (PLND) area. MATERIALS AND METHODS: The study was approved by the institutional review boards at all four hospitals; patients provided written informed consent. Two hundred ninety-six consecutive men (mean age, 67 years; range, 47-83 years) with prostate cancer and an intermediate-to-high risk for nodal metastases (prostate-specific antigen level >10 ng/mL, Gleason score >6, or stage T3 disease) were enrolled. MR lymphography of the pelvis was performed 24 hours after intravenous drip infusion of ferumoxtran-10. Positive nodes at MR lymphography were indicated to be inside or outside the routine dissection area (RDA). On the basis of MR lymphography computed tomographic (CT)-guided biopsy, routine PLND, or MR imaging-guided minimal extended PLND was performed. RESULTS: MR lymphography findings were positive in 58 patients. Of these, 44 had histopathologic confirmation of lymph node metastases. In 18 of 44 patients (41%), MR lymphography findings showed nodes exclusively outside the RDA, which were confirmed with MR lymphography-guided extended PLND (n = 13) and CT-guided biopsy (n = 5). In another 18 patients (41%), positive nodes were located both inside and outside the RDA at MR lymphography. In these 18 patients, routine PLND was used to confirm the nodes inside the RDA (n = 11); CT-guided biopsy was used to confirm nodes outside the RDA (n = 7). In the remaining eight patients, MR lymphography findings showed only nodes inside the RDA, which was confirmed with PLND (n = 5) and CT-guided biopsy (n = 3). In 14 of the 58 patients (24%), there was no histologic confirmation. CONCLUSION: In 41% of patients with prostate cancer, nodal metastases outside the area of routine PLND were detected by using MR imaging with ferumoxtran-10.

Stem cells and the natural history of lung cancer: implications for lung cancer screening.

Lung cancer is not simply a single disease, but a collection of several phenotypically very diverse and regionally distinct neoplasias. Its natural history is complex and not yet fully understood. Stem cells and the complex interaction with the microenvironment of the tumor and the immune system play an important role in tumor progression and metastasizing capacity. This finding explains why lung cancer does not always follow the multistep carcinogenetic and exponential growth model and why small lesions do not always equate to early-stage disease. Despite the fact that volume doubling times are increasingly used as surrogate markers for the natural history of lung cancer and as estimates for the proportion of overdiagnosed cases, it is only a momentary impression. At baseline screening especially, screen-detected lung cancer cases are preferably detected when they are in the indolent phase of their growth curve (length-biased sampling), from which it can by no means be concluded that they may not progress or metastasize at a later stage. Because the natural history of lung cancer is only partly elucidated, conclusions on the impact of overdiagnosis in lung cancer screening are premature.

Automatic segmentation of pulmonary segments from volumetric chest CT scans.

Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segment the pulmonary segments with high accuracy (77%), comparable to both interobserver and intraobserver accuracy (74% and 80%, respectively).