Comparing CT colonography and flexible sigmoidoscopy: a randomised trial within a population-based screening programme.

IMPORTANCE AND AIMS: The role of CT colonography (CTC) as a colorectal cancer (CRC) screening test is uncertain. The aim of our trial was to compare participation and detection rate (DR) with sigmoidoscopy (flexible sigmoidoscopy (FS)) and CTC in a screening setting. DESIGN SETTING AND PARTICIPANTS: We conducted two randomised clinical trials (RCTs). (1) Participation RCT: individuals, aged 58 years, living in Turin (Italy), were randomly assigned to be invited to FS or CTC screening; (2) detection RCT: residents in northern Italy, aged 58-60, giving their consent to recruitment, were randomly allocated to CTC or FS. Polyps ≥6 mm at CTC, or 'high-risk' distal lesions at FS, were referred for colonoscopy (TC). MAIN OUTCOME MEASURES: Participation rate (proportion of invitees examined); DR of advanced adenomas or CRC (advanced neoplasia (AN)). RESULTS: Participation was 30.4% (298/980) for CTC and 27.4% (267/976) for FS (relative risk (RR) 1.1; 95% CI 0.98 to 1.29). Among men, participation was higher with CTC than with FS (34.1% vs 26.5%, p=0.011). In the detection RCT, 2673 subjects had FS and 2595 had CTC: the AN DR was 4.8% (127/2673, including 9 CRCs) with FS and 5.1% (133/2595, including 10 CRCs) with CTC (RR 1.08; 95% CI 0.85 to 1.37). Distal AN DR was 3.9% (109/2673) with FS and 2.9% (76/2595) with CTC (RR 0.72; 95% CI 0.54 to 0.96); proximal AN DR was 1.2% (34/2595) for FS vs 2.7% (69/2595) for CTC (RR 2.06; 95% CI 1.37 to 3.10). CONCLUSIONS AND RELEVANCE: Participation and DR for FS and CTC were comparable. AN DR was twice as high in the proximal colon and lower in the distal colon with CTC than with FS. Men were more likely to participate in CTC screening. TRIAL REGISTRATION NUMBER: NCT01739608; Pre-results.

Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction.

OBJECTIVES: To provide multicentre external validation of the Bayesian Inference Malignancy Calculator (BIMC) model by assessing diagnostic accuracy in a cohort of solitary pulmonary nodules (SPNs) collected in a clinic-based setting. To assess model impact on SPN decision analysis and to compare findings with those obtained via the Mayo Clinic model. METHODS: Clinical and imaging data were retrospectively collected from 200 patients from three centres. Accuracy was assessed by means of receiver-operating characteristic (ROC) areas under the curve (AUCs). Decision analysis was performed by adopting both the American College of Chest Physicians (ACCP) and the British Thoracic Society (BTS) risk thresholds. RESULTS: ROC analysis showed an AUC of 0.880 (95 % CI, 0.832-0.928) for the BIMC model and of 0.604 (95 % CI, 0.524-0.683) for the Mayo Clinic model. Difference was 0.276 (95 % CI, 0.190-0.363, P < 0.0001). Decision analysis showed a slightly reduced number of false-negative and false-positive results when using ACCP risk thresholds. CONCLUSIONS: The BIMC model proved to be an accurate tool when characterising SPNs. In a clinical setting it can distinguish malignancies from benign nodules with minimal errors by adopting current ACCP or BTS risk thresholds and guiding lesion-tailored diagnostic and interventional procedures during the work-up. KEY POINTS: • The BIMC model can accurately discriminate malignancies in the clinical setting • The BIMC model showed ROC AUC of 0.880 in this multicentre study • The BIMC model compares favourably with the Mayo Clinic model.

ypN0: Does It Matter How You Get There? Nodal Downstaging in Esophageal Cancer.

BACKGROUND: ypN0 following induction treatment for advanced esophageal cancer improves survival. Importance of how ypN0 is achieved is unknown. This study evaluates survival in "natural" N0 (cN0/ypN0) and "downstaged" N0 (cN+/ypN0) patients. METHODS: Among patients treated with induction treatment and surgery, 83 CT scans were retrieved in digital format and re-evaluated by a radiologist, blinded to pathological nodal status: 28 natural N0, 37 downstaged N0, and 18 ypN+. Impact of N0 classification on survival and associations with survival were identified. RESULTS: Survival varied with ypN: 3-year survival was 84 % for natural N0 patients, 59 % for downstaged N0, and 20 % for ypN+ (p < .001). Compared with natural N0 patients, risk of cancer mortality was 3.8 for downstaged N0 and 7.6 for ypN+ (p = .01). Survival was also stratified by ypT: compared with ypT0 natural N0, who had the best survival, intermediate survival was seen in ypT+ natural N0 [hazard ratio (HR), 1.3] and ypT0 downstaged N0 (HR, 1.8), and poor survival in ypT+ downstaged N0 (HR, 9.5) and ypN+ (HR, 12.0) (p = .026). CONCLUSIONS: Natural N0 and downstaged N0 patients are different clinical entities: downstaging cN+ with induction treatment producing downstaged N0 improves survival only if there is concomitant primary cancer downstaging to ypT0. Intermediate survival is seen in downstaged N0 patients with complete tumor response. Natural N0 patients experience intermediate survival with incomplete response (ypT+). Complete response in natural N0 patients produces the best survival. Means of obtaining ypN0 status matters and requires a complete response for downstaged N0 patients to benefit from induction treatment.

Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.

OBJECTIVES: The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models. METHODS: A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model's fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines. RESULTS: ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC). CONCLUSIONS: Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results. KEY POINTS: • The BIMC and PKUPH models offer better characterization than older prediction models • Both the PKUPH and BIMC models completely avoided false negative results • The Mayo model suffers from a large number of indeterminate results.

The Role of Computed Tomography Colonography in Detecting Bowel Involvement in Women With Deep Infiltrating Endometriosis: Comparison With Clinical History, Serum Ca125, and Transvaginal Sonography.

OBJECTIVE: We wanted to assess the diagnostic value of computed tomographic colonography (CTC) in recognizing bowel endometriosis in comparison with serum Ca125, transvaginal sonography (TVS), and presence of intestinal symptoms. METHODS: We included in this study 92 women undergoing surgery for symptomatic DIE. Preoperative evaluation included clinical history, Ca125 serum value, and TVS. CTC was performed in 37/92 patients (40.2%), and the results were compared to the other preoperative tools and to surgical exploration, considered the clinical reference standard. RESULTS: Surgery confirmed bowel endometriosis in 49/92 subjects (53.3%). Presence of intestinal symptoms, serum Ca125 values, and TVS were significantly correlated to intestinal involvement, but CTC had the highest accuracy in detecting bowel endometriosis with a sensitivity of 68%, a specificity of 67%, a PPV of 81%, and a NPV of 50% (P = 0.04). CONCLUSIONS: CTC proved to be an accurate and low invasive imaging technique to detect DIE of the bowel and compared favorably with clinical evaluation, serum Ca125 determination, and TVS.

CT Imaging Features in the Characterization of Non-Growing Solid Pulmonary Nodules in Non-Smokers.

BACKGROUND: A disappearing or persistent solid pulmonary nodule is a neglected clinical entity that still poses serious interpretative issues to date. Traditional knowledge deriving from previous reports suggests particular features, such as smooth edges or regular shape, to be significantly associated with benignity. A large number of benign nodules are reported among smokers in lung cancer screening programmes. The aim of this single-center retrospective study was to correlate specific imaging features to verify if traditional knowledge as well as more recent acquisitions regarding benign SPNs can be considered reliable in a current case series of nodules collected in a non-smoker cohort of patients. MATERIAL/METHODS: Fifty-three solid SPNs proven as non-growing during follow-up imaging were analyzed with regard to their imaging features at thin-section CT, their predicted malignancy risk according to three major risk assessment models, minimum density analysis and contrast enhanced-CT in the relative subgroups of nodules which underwent such tests. RESULTS: Eleven nodules disappeared during follow-up, 29 showed volume loss and 16 had a VDT of 1121 days or higher. There were 48 nodules located peripherally (85.71%). Evaluation of the enhancement after contrast media (n=29) showed mean enhancement ±SD of 25.72±35.03 HU, median of 18 HU, ranging from 0 to 190 HU. Minimum density assessment (n=30) showed mean minimum HU ±SD of -28.27±47.86 HU, median of -25 HU, ranging from -144 to 68 HU. Mean malignancy risk ±SD was 15.05±26.69% for the BIMC model, 17.22±19.00% for the Mayo Clinic model and 19.07±33.16% for the Gurney's model. CONCLUSIONS: Our analysis suggests caution in using traditional knowledge when dealing with current small solid peripheral indeterminate SPNs and highlights how quantitative growth at follow-up should be the cornerstone of characterization.

Distribution of Solid Solitary Pulmonary Nodules within the Lungs on Computed Tomography: A Review of 208 Consecutive Lesions of Biopsy-Proven Nature.

BACKGROUND: The solitary pulmonary nodule (SPN) is a common radiologic abnormality on chest x-rays or computed tomography (CT) scans of the lungs. The differential diagnosis of SPNs is particularly wide as it includes a multitude of benign as well as malignant entities. Nodule location within the lungs has been proposed as a predictive feature in the literature. This study aims at illustrating the distribution within the lungs of a large current series of consecutive SPNs according to their histological subtype, which was definitely proved at core biopsy. MATERIAL/METHODS: Two hundred-eight SPNs referred to our center for characterization were reviewed in this single-centre retrospective study. Histological subtypes were defined following the IASLC/ATS/ERS and WHO (2004) histological classification. RESULTS: This study provides evidence with respect to the prevalence of adenocarcinomas and other non-neuroendocrine primary lung cancer types in the right upper lobe. It also provides new evidence with respect to the prevalence of carcinoid tumors in the middle and right lower lobe, with a tendency to occur in the central lung parenchyma. CONCLUSIONS: This work updates existing knowledge of solid SPNs location within the lungs by providing a current picture of SPN distribution according to their nature.

Integrating supportive care into the multidisciplinary management of lung cancer: we can't wait any longer.

INTRODUCTION: Due to important achievements in terms of diagnostic and therapeutic tools and the complexity of the disease itself, lung cancer management needs a multidisciplinary approach. To date, the classical multidisciplinary team involves different healthcare providers mainly dedicated to lung cancer diagnosis and treatments. Nevertheless, the underlying disease and related treatments significantly impact on patient function and psychological well-being. In this sense, supportive care may offer the best approach to relieve and manage patient symptoms and treatment-related adverse events. AREAS COVERED: Evidence reports that exercise, nutrition, smoking cessation, and psychological well-being bring many benefits in patients with lung cancer, from both a physical and socio-psychological points of view, and potentially improving their survival. Nevertheless, supportive care is rarely offered to patients, and even less frequently these needs are discussed within the multidisciplinary meeting. EXPERT OPINION: Integrating supportive care as part of the standard multidisciplinary approach for lung cancer involves a series of challenges, the first one represented by the daily necessity of specialists, such as kinesiologists, dietitians, psycho-oncologists, able to deliver a personalized approach. In the era of precision medicine, this is an essential step forward to guarantee comprehensive and patient-centered care for all patients with lung cancer.

Main pancreatic duct intraductal papillary mucinous neoplasms: accuracy of MR imaging in differentiation between benign and malignant tumors compared with histopathologic analysis.

PURPOSE: To retrospectively determine the accuracy of magnetic resonance (MR) imaging combined with MR cholangiopancreatography (CP) in differentiating benign from malignant intraductal papillary mucinous neoplasms (IPMNs) involving the main pancreatic duct (MPD), with histopathologic analysis as the reference standard. MATERIALS AND METHODS: The informed consent requirement was waived for this institutional review board-approved study. A total of 51 patients with histopathologically proved IPMNs (MPD IPMN, n = 29; mixed type IPMN, n = 22), underwent MR imaging, MR CP, and surgery, with a mean interval of 2.6 months between MR examination and surgery. Qualitative image analysis included assessment of the site of MPD dilatation (head of the pancreas, body and/or tail of the pancreas, or diffuse), presence or absence of duct wall nodules, and contrast enhancement of the MPD walls. Quantitative image analysis included measurement of the maximum diameter of the MPD. A comparison of adenomas and borderline IPMNs with cancerous IPMNs was performed with the Student t test or the Mann-Whitney U test for continuous variables. RESULTS: At histopathologic analysis, 27 IPMNs were classified as carcinomas; 13, as borderline tumors; and 11, as adenomas. MPD wall nodules were observed in 16 carcinomas involving the MPD and one adenoma or borderline neoplasm (P < .00001). Duct wall enhancement was observed in 20 MPD or mixed type carcinomas and five adenomas or borderline neoplasms (P = .0001). The median maximal diameter of the MPD was 18 mm in malignant MPD or mixed type IPMNs and 11 mm in benign borderline IPMNs (P = .038). No significant difference in the overall 5-year survival rate of patients with MPD IPMNs and those with mixed type IPMNs was observed (P = .813). CONCLUSION: Duct wall nodules and enhancement of the MPD walls are signs of malignant MPD or mixed type IPMNs. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.2531080604/-/DC1.

Thyroid volumetric quantification: comparative evaluation between conventional and volumetric ultrasonography.

OBJECTIVE: Thyroid volume quantification is an important parameter for radiotherapy dosing in cases of major thyroid diseases such as thyroiditis and carcinoma. In clinical practice, this calculation is performed by means of ultrasonography on the basis of an ellipsoid formula obtained from the 3 axes. The aim of our study was to compare the accuracy of volume calculation between B-mode ultrasonography and volumetric ultrasonography (VUS). METHODS: Between April and May 2007, 27 consecutive patients selected for thyroidectomy were prospectively evaluated. One expert ultrasound operator calculated each thyroid volume with standard B-mode ultrasonography on the basis of the 3 axes of each lobe, and then the patients were analyzed with an offline workstation equipped with volumetric probes (VUS). On the offline workstation, 2 separate blinded operators (VUS1 and VUS2) calculated the thyroid volume with virtual organ computer-aided analysis. Data acquired were then compared with pathologic anatomy (PA). RESULTS: The mean time for B-mode analysis was 6 minutes, whereas VUS analysis needed a mean time of 16.5 minutes. Interobserver variability between the median VUS1 and VUS2 measurements was 0.36 mL (interquartile range [IQR], -0.79 to 0.37 mL; P < .156). The median variability between B-mode ultrasonography and PA was -9.6 mL (IQR, -16.7 to 1.5 mL; P < .001), and that between VUS and PA was -2.87 mL (IQR, -11.97 to 9.51 mL; P = .019). The overall performance of B-mode ultrasonography in comparison with PA was -29.1% (IQR, -47.5% to -5.9%), and that of VUS in comparison with PA was -6.3% (IQR, -26.3 to 13.7%; P < .001). CONCLUSIONS: Volumetric ultrasonography is a valid tool that compares better with PA than does B-mode ultrasonography.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis.

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.