The knowledge of the status of axillary lymph nodes (LN) of patients with breast cancer is a fundamental prerequisite in the therapeutic decision. In the present work, we evaluated the impact of fine-needle aspiration cytology (FNAC) of ultrasonographically (US) selected axillary LN in the diagnosis of LN metastases and subsequently in the treatment of patients with breast cancer. Axillary US was performed in 298 patients with diagnosed breast cancer (267 invasive carcinomas and 31 ductal carcinoma in situ DCIS), and in 95 patients it was followed by FNAC of US suspicious LN. Smears were examined by routine cytological staining. Cases of uncertain diagnosis were stained in immunocytochemistry (ICC) with a combination of anticytokeratin and anti-HMFG2 antibodies. Eighty-five FNAC were informative (49 LN were positive for metastases, 36 were negative). In 49 of 267 patients with invasive breast carcinoma (18%), a preoperative diagnosis of metastatic LN in the axilla could be confirmed. These patients could proceed directly to axillary dissection. In addition, US-guided FNAC presurgically scored 49 out of 88 (55%) metastatic LN. Of all others, with nonsuspicious LN on US (203 cases including 31 DCIS), in which no FNAC examination was performed, 28 invasive carcinomas (16%) turned out to be LN positive on histological examination. Based on these data, US examination should be performed in all patients with breast cancer adding ICC-supported FNAC only on US-suspect LN. This presurgical protocol is reliable for screening patients with LN metastases that should proceed directly to axillary dissection or adjuvant chemotherapy, thus avoiding sentinel lymph node biopsy.

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We evaluated the effectiveness and the cost of axillary staging in breast cancer patients by ultrasound-guided fine-needle aspiration cytology (US-FNAC), sentinel node biopsy (SNB), and frozen sections of the sentinel node to achieve the target of the highest number of immediate axillary dissections. From January 2003 through October 2005, a total of 404 consecutive eligible breast cancer patients underwent US-FNAC of suspicious axillary lymph nodes. If tumor cells were found, immediate axillary dissection was proposed (33% of node-positive cases). If US or cytology was negative, SNB was performed. Frozen sections of the sentinel node allowed immediate axillary dissection in 31% of node-positive cases. The remaining 36% underwent delayed axillary dissection. We compared our policy with clinical evaluation of the axilla, showing better specificity of US-FNAC, the cost balanced by a 12% reduction of SNBs, and a marked reduction of unnecessary axillary dissections resulting from false-positive clinical staging. Moreover, the comparison between our policy and permanent histology of the sentinel node showed an 8% cost saving, mainly associated with the immediate axillary dissections. US-FNAC of axillary lymph nodes in breast cancer patients reliably predicts the presence of metastases and therefore refers a significant number of patients to the appropriate surgical treatment, avoiding an SNB. As cost saving to the health care system in our study is mainly related to one-step axillary surgery, US-FNAC of axillary lymph nodes and frozen section of the sentinel node generate significant cost saving for patients who have metastatic nodes.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

The MAGIC-5 Project aims at developing Computer Aided Detection (CAD) software for Medical Applications on distributed databases by means of a GRID Infrastructure Connection. The use of automatic systems for analyzing medical images is of paramount importance in the screening programs, due to the huge amount of data to check. Examples are: mammographies for breast cancer detection, Computed-Tomography (CT) images for lung cancer analysis, and the Positron Emission Tomography (PET) imaging for the early diagnosis of the Alzheimer disease. The need for acquiring and analyzing data stored in different locations requires a GRID approach of distributed computing system and associated data management. The GRID technologies allow remote image analysis and interactive online diagnosis, with a relevant reduction of the delays actually associated to the screening programs. From this point of view, the MAGIC-5 collaboration can be seen as a group of distributed users sharing their resources for implementing different Virtual Organizations (VO), each one aiming at developing screening programs, tele-training, tele-diagnosis and epidemiologic studies for a particular pathology.