Radionuclide imaging of bone marrow disorders

Noninvasive imaging techniques have been used in the past for visualization the functional activity of the bone marrow compartment. Imaging with radiolabelled compounds may allow different bone marrow disorders to be distinguished. These imaging techniques, almost all of which use radionuclide-labelled tracers, such as 99mTc-nanocolloid, 99mTc-sulphur colloid, 111In-chloride, and radiolabelled white blood cells, have been used in nuclear medicine for several decades. With these techniques three separate compartments can be recognized including the reticuloendothelial system, the erythroid compartment and the myeloid compartment. Recent developments in research and the clinical use of PET tracers have made possible the analysis of additional properties such as cellular metabolism and proliferative activity, using 18F-FDG and 18F-FLT. These tracers may lead to better quantification and targeting of different cell systems in the bone marrow. In this review the imaging of different bone marrow targets with radionuclides including PET tracers in various bone marrow diseases are discussed

Automated detection of lung nodules in low-dose computed tomography

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector computed-tomography (CT) images has been developed in the framework of the MAGIC-5 Italian project. One of the main goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis through a data and cpu GRID infrastructure. The basic modules of our lungCAD system, consisting in a 3D dot-enhancement filter for nodule detection and a neural classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and sub-pleural nodules. The database used in this study consists of 17 low-dose CT scans reconstructed with thin slice thickness ( 3c300 slices/scan). The preliminary results are shown in terms of the FROC analysis reporting a good sensitivity (85% range) for both internal and sub-pleural nodules at an acceptable level of false positive findings (1-9 FP/scan); the sensitivity value remains very high (75% range) even at 1-6 FP/scan

Efficacy and safety of chronomodulated irinotecan, oxaliplatin, 5‐fluorouracil and leucovorin combination as first‐ or second‐line treatment against metastatic colorectal cancer : results from the International EORTC 05011 Trial

The triplet combination of irinotecan, oxaliplatin and fluorouracil is an active frontline regimen in metastatic colorectal cancer, but scarce data exist on its use as salvage treatment. We aimed at assessing its safety and efficacy profiles with its circadian‐based administration (chronoIFLO5) as either first‐ or second‐line treatment, within the time‐finding EORTC 05011 trial. Five‐day chronoIFLO5 was administered every 3 weeks in patients with PS 0, 1 or 2. It consisted of chronomodulated irinotecan (180 mg/sqm), oxaliplatin (80 mg/sqm) and fluorouracil‐leucovorin (2800 and 1200 mg/sqm, respectively). For our study, toxicity and antitumour activity were evaluated separately in first‐ and second‐line settings. Primary endpoints included Grade 3‐4 toxicity rates, best objective response rate (ORR), progression‐free survival (PFS) and overall survival (OS). One‐hundred forty‐nine and 44 patients were treated in first‐line and second‐line settings, respectively, with a total of 1138 cycles with median relative dose intensities of about 90%. Demographics were comparable in the two groups. Thirty‐six (24.7%) and 10 (22.2%) patients experienced at least one episode of severe toxicity in first line and second line, respectively. Frontline chronoIFLO5 yielded an ORR of 62.3% [95% CI: 54.2‐70.4] and resulted in median PFS and OS of 8.7 months [7.5‐9.9] and 19.9 months [15.4‐24.5]. Corresponding figures in second line were 37.5% [22.5‐52.5], 6.7 months [4.8‐8.9] and 16.3 months [11.8‐20.8]. International and prospective evaluation revealed the favourable safety and efficacy profiles of chronoIFLO5, both as frontline and as salvage treatment against metastatic colorectal cancer. In particular, encouraging activity in second line was observed, with limited haematological toxicity

Preprocessing methods for nodule detection in lung CT

The use of automatic systems in the analysis of medical images has proven to be very useful to radiologists, especially in the framework of screening programs, in which radiologists make their first diagnosis on the basis of images only, most of those corresponding to healthy patients, and have to distinguish pathological findings from non-pathological ones at an early stage. In particular, we are developing preprocessing methods to be applied for pulmonary nodule Computer Aided Detection in low-dose lung Multi Slice CT (computed tomography) images. (c) 2005 CARS & Elsevier B.V. All rights reserved

A massive lesion detection algorithm in mammography

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency. (c) 2007 American Association of Physicists in Medicine

A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency

A massive lesion detection algorithm in mammography

A new algorithm for massive lesion detection in mammography is presented. The algorithm consists in three main steps : 1) reduction of the dimension of the image to be processed through the identifi cation of regions of interest (rois) as candidates for massive lesions ; 2) characterization of the roi by means of suitable feature extraction ; 3) pattern classifi cation through supervised neural networks. Suspect regions are detected by searching for local maxima of the pixel grey level intensity. A ring of increasing radius, centered on a maximum, is considered until the mean intensity in the ring decreases to a defi ned fraction of the maximum. The rois thus obtained are described by average, variance, skewness and kurtosis of the intensity distributions at diff erent fractions of the radius. A neural network approach is adopted to classify suspect pathological and healthy pattern. The software has been designed in the framework of the infn (Istituto Nazionale Fisica Nucleare) research project gpcalma (Grid Platform for calma) which recruits physicists and radiologists from diff erent Italian Research Institutions and hospitals to develop software for breast and lung cancer detection