Primary carcinoma of the lung overlooked at CT: analysis of findings in 14 patients

Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect

PURPOSE

To assess for change in the 1990s in the failure of detection at chest radiography of potentially resectable non-small cell lung cancer (NSCLC) lesions compared with experience in the previous decade.

MATERIALS AND METHODS

From 1993 to 2001, an observational cohort was identified that consisted of 40 instances of NSCLC evident retrospectively at chest radiography but undetected by a radiologist at a time when the cancer was potentially resectable for cure. Sizes and locations of the tumors were assessed. Pearson chi(2) testing was performed to compare the sex distribution of lung cancer in the present series with population data for the sex distribution of lung cancer in the United States during the present study.

RESULTS

Twenty-five (62%) undetected NSCLCs were in men and 15 (38%) were in women, yielding a ratio not significantly different from that for the sex distribution of NSCLC according to national data (chi(2) = 0.22, P =.64). Median patient age was 62 years (range, 37-87 years). Median diameter of the missed cancers was 1.9 cm. Missed cancers were most commonly located in the upper lobes (right, 45%; left, 28%; total, 72%), especially in the apical and posterior segments/subsegments (60% of all the missed cancers). A clavicle obscured 22% of the missed cancers. Eighty-five percent of the missed cancers were in peripheral locations.

CONCLUSION

Potentially resectable NSCLC lesions missed at chest radiography were characterized by predominantly peripheral (85%) and upper lobe (72%) locations and by apical and posterior segmental/subsegmental locations in an upper lobe (60%). Distribution by sex of the missed cancers was comparable to national data for NSCLC. The missed cancers had a median diameter of 1.9 cm.

Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings

PURPOSE

To compare clinical, histopathologic, and imaging features of lung cancers missed at low-radiation-dose helical computed tomography (CT).

MATERIALS AND METHODS

Eighty-three primary lung cancers were found during an annual low-dose CT screening program and confirmed histopathologically at either surgery or biopsy. Thirty-two of these lung cancers were missed on 39 CT scans: on 23 scans owing to detection errors and on 16 owing to interpretation errors. The clinical characteristics, CT features, and histopathologic findings of these missed lung cancers were correlated.

RESULTS

All missed cancers were intrapulmonary, and 28 (88%) were stage IA. All 20 detection errors occurred in cases of adenocarcinoma, 17 (85%) of which were well-differentiated tumors and 11 (55%) of which were in nonsmoking women. The mean size of cancers missed owing to detection error, 9.8 mm, was smaller than that of cancers missed owing to interpretation error, 15.9 mm (P <.001). In the detection error group, the percentages of nodules with ground-glass opacity (91%) or judged to be subtle (91%) were greater than those of nodules in the interpretation error group (38% and 25%, respectively) (P <.001). In the detection error group, 83% (19/23) of cancers were overlapped with, obscured by, or similar in appearance to normal structures such as pulmonary vessels. On 14 of the 16 CT scans with which there were interpretation errors, the CT findings mimicked benign disease, and the patients also had underlying lung disease, such as tuberculosis, emphysema, or lung fibrosis.

CONCLUSION

The lung cancers missed at low-dose CT screening in this series generally were very subtle and appeared as small faint nodules, overlapping normal structures, or opacities in a complex background of other disease.

Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program

PURPOSE

To evaluate the performance of a fully automated computerized method for the detection of lung nodules in computed tomographic (CT) scans in the identification of lung cancers that may be missed during visual interpretation.

MATERIALS AND METHODS

A database of 38 low-dose CT scans with 50 lung nodules was obtained from a lung cancer screening program. Thirty-eight of the nodules represented biopsy-confirmed lung cancers that had not been reported during initial clinical interpretation. A computer detection method that involved the use of gray-level thresholding techniques to identify three-dimensionally contiguous structures within the lungs was applied to the CT data. Computer-extracted volume was used to determine whether a structure became a nodule candidate. A rule-based scheme and a cascaded automated classifier were applied to the set of nodule candidates to distinguish actual nodules from areas of normal anatomy. Overall performance of the computer detection method was evaluated with free-response receiver operating characteristic (FROC) analysis.

RESULTS

At a specific operating point on the FROC curve, the method achieved a sensitivity of 80% (40 of 50 nodules), with an average of 1.0 false-positive detection per section. Missed cancers were detected by the computerized method with a sensitivity of 84% (32 of 38 nodules) and a false-positive rate of 1.0 per section.

CONCLUSION

With an automated lung nodule detection method, a large fraction (84%, 32 of 38) of missed cancers in a database of low-dose CT scans were detected correctly.

Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system

We are developing a computer-aided diagnosis (CAD) system for lung nodule detection on thoracic helical computed tomography (CT) images. In the first stage of this CAD system, lung regions are identified by a k-means clustering technique. Each lung slice is classified as belonging to the upper, middle, or the lower part of the lung volume. Within each lung region, structures are segmented again using weighted k-means clustering. These structures may include true lung nodules and normal structures consisting mainly of blood vessels. Rule-based classifiers are designed to distinguish nodules and normal structures using 2D and 3D features. After rule-based classification, linear discriminant analysis (LDA) is used to further reduce the number of false positive (FP) objects. We performed a preliminary study using 1454 CT slices from 34 patients with 63 lung nodules. When only LDA classification was applied to the segmented objects, the sensitivity was 84% (53/63) with 5.48 (7961/1454) FP objects per slice. When rule-based classification was used before LDA, the free response receiver operating characteristic (FROC) curve improved over the entire sensitivity and specificity ranges of interest. In particular, the FP rate decreased to 1.74 (2530/1454) objects per slice at the same sensitivity. Thus, compared to FP reduction with LDA alone, the inclusion of rule-based classification lead to an improvement in detection accuracy for the CAD system. These preliminary results demonstrate the feasibility of our approach to lung nodule detection and FP reduction on CT images.

Superficial endobronchial lung cancer: radiologic-pathologic correlation

OBJECTIVE

To analyze the plain chest radiographic and CT findings of superficial endobronchial lung cancer and to correlate these with the findings of histopathology.

MATERIALS AND METHODS

This study involved 19 consecutive patients with pathologically proven lung cancer confined to the bronchial wall. Chest radiographs and CT scans were reviewed for the presence of parenchymal abnormalities, endobronchial nodules, bronchial obstruction, and bronchial wall thickening and stenosis. The CT and histopathologic findings were compared.

RESULTS

Sixteen of the 19 patients had abnormal chest radiographic findings, while in 15 (79%), CT revealed bronchial abnormalities: an endobronchial nodule in seven, bronchial obstruction in five, and bronchial wall thickening and stenosis in three. Histopathologically, the lesions appeared as endobronchial nodules in 11 patients, irregular thickening of the bronchial wall in six, elevated mucosa in one, and carcinoma in situ in one.

CONCLUSION

CT helps detect superficial endobronchial lung cancer in 79% of these patients, though there is some disagreement between the CT findings and the pathologic pattern of bronchial lesions. Although nonspecific, findings of bronchial obstruction or bronchial wall thickening and stenosis should not be overlooked, and if clinically necessary, bronchoscopy should be performed.

Detection of lung cancer on the chest radiograph: impact of previous films, clinical information, double reading, and dual reading

To study the impact of clinical information, previous chest radiographs, double reading (one pair of observers read the films independently) and dual reading (one pair of observers read the films simultaneously) on the sensitivity, specificity and odds ratios of observers in the detection of early lung cancer on the chest radiograph. The study was performed in 3 sessions. In the first session, 14 observers reviewed the chest radiographs of 100 cases (30 with early lung cancer, 35 with no abnormalities, 35 with other cardiopulmonary diseases). Sensitivity, specificity, odds ratios and the effect of double reading were calculated. After 4 months, a second session was held in which 7 observers reviewed all cases with the provision of all information. The other 7 observers reviewed all cases without information again. To determine the effect of dual reading, in the third session, 4 pairs of observers reviewed all cases in which they disagreed in the first independent reading session. The effects of information, double reading and dual reading on sensitivity, specificity and odds ratio were calculated. The sensitivity of the observers in the first session ranged from 0.20 to 0.60 and the specificity from 0.87 to 0.95. In the second session, these parameters changed only slightly, independent from the availability of clinical information and previous films. With double reading the sensitivity increased and specificity decreased. With dual reading sensitivity increased and specificity remained unchanged. The odds ratios were improved with double reading by 4% and with the dual reading by 14%. Additional clinical information, previous chest radiographs, double reading and dual reading have little impact on the detection of early lung cancer on the chest radiograph.

Automated detection of lung nodules in CT scans: preliminary results

We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate. After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to nonnodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.

Perceived features reported as nodules: interpretation of spiral chest CT scans

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate nontarget locations identified in a study of lung nodule detection with spiral computed tomographic (CT) scans that compared cine and film presentations.

MATERIALS AND METHODS

In a previous study of lung nodule detection, eight observers were asked to identify 10 nodule locations in each of five CT scans containing eight simulated nodules. In the current study, each nontarget location that was reported more than once in the previous study was inspected with a stack-mode display in both cine and static modes. The nontarget locations were evaluated for probable identity, shape, and distance from the peripheral lung surface.

RESULTS

Fifty-two nontarget locations included clinically undetected pulmonary nodules (n = 12), lymph nodes (n = 2), unclassifiable structures (n = 2), pleural scars (n = 8), and vascular structures (n = 28). Five nontarget locations contained vessels with complex courses apparent only with cine mode. As a group, nontarget locations were significantly closer to the periphery than would be expected by chance (for all locations, P < .0001; for locations not touching the pleural surface, P = .013).

CONCLUSION

The lower reporting threshold caused by the observer instructions to find 10 targets resulted in increased reporting of structure with a nodular appearance. The locations of these reports in the lung periphery can be attributed to the relationship between frequent disease and a nearly featureless background in the lung periphery.

Detection failures in spiral CT screening for lung cancer: analysis of CT findings

PURPOSE

To clarify the computed tomographic (CT) findings and the progression of minute lung cancers that were missed at initial spiral CT screening but were later detected.

MATERIALS AND METHODS

The findings from seven patients with lung cancer that was missed at the initial spiral CT screening were reviewed. Retrospective CT findings, time to detection, cell type, and pathologic stage were evaluated.

RESULTS

Minute lung cancers missed at early spiral CT included a nodule among the shadows of old tuberculosis (n = 2), a faint nodule with high attenuation in the center of the nodule (n = 1), an increase in attenuation just adjacent to an axial peripheral pulmonary vessel (n = 1) and adjacent to a craniocaudal peripheral pulmonary vessel (n = 1), and a minute faint nodule (n = 2). The time to detection ranged from 6 to 18 months. At pathologic examination, six cancers were stage I, and one was stage II.

CONCLUSION

Minute nodules of lung cancer that are near the threshold of detectability may be missed at spiral CT screening. It is important to examine noncalcified nodules with thin-section CT even when lesions from prior disease, such as those from old tuberculosis, exist and to evaluate the shadows of pulmonary vessels carefully. A follow-up examination is highly recommended.

Can chest CT decrease the use of preoperative bronchoscopy in the evaluation of suspected bronchogenic carcinoma?

BACKGROUND

Fiberoptic bronchoscopy (FOB) is frequently used to diagnose and stage bronchogenic carcinoma (BC). However, the value of FOB in diagnosis/staging BC presenting as a pulmonary nodule or mass (PNM) is controversial. Since chest CT is usually obtained in these patients, it may be used in patient selection for preoperative FOB.

OBJECTIVE

Evaluation of the role of chest CT in determining the predictive value of FOB in diagnosing/staging a PNM, by comparing the results of CT and bronchoscopy.

DESIGN

Retrospective review of chest CTs and medical records.

PATIENTS

Consecutive patients with BC between 1992 and 1994 who had diagnostic FOB and CT in our institution, but without radiographic evidence of (1) pulmonary atelectasis, (2) endobronchial tumor or narrowing of the central airways, and (3) the PNM abutting the central airways.

RESULTS

Sixty-four patients met the selection criteria. The size of the PNM ranged from 1.5 to 10 cm; the size was < or = 4 cm in 62 patients. FOB provided a diagnosis in 22 patients. Bronchoscopy detected endobronchial lesions in 11 patients (17%); 3 had lesions in more than one lobe. In three patients, the PNM was <3 cm. The radiographically undetected endobronchial tumor increased the tumor stage in only two patients. The "CT bronchus" sign had a positive and negative predictive value of 75% and 68%, respectively.

CONCLUSIONS

(1) In this study, CT failed to detect endobronchial tumor in 11 of 64 patients (17%). Because of the implications of a new staging system, more studies are necessary before abandoning staging FOB. (2) The CT bronchus sign has a very high positive and negative predictive value in the use of diagnostic FOB and should be used to guide the method of biopsy of a PNM.