The MAGIC-5 project: medical applications on a grid infrastructure connection

Bellotti, Roberto; Bagnasco, S.; Bottigli, U.; Castellano, M.; Cataldo, Rosella; Catanzariti, Ezio; Cerello, P.; Cheran, S. C.; Carlo, Francesco De; Delogu, Pasquale; Mitri, I. De; Nunzio, Giorgio De; Fantacci, M. E.; Fauci, F.; Forni, G.; Gargano, G.; Golosio, Bruno; Indovina, Pietro Luigi; Lauria, A.; Torres, E. López; Magro, R.; Martello, D.; Masala, G. L.; Massafra, Raffaella; Oliva, Piernicola; Palmiero, Rosa; Martinez, A. Preite; Prevete, R.; Quarta, Maurizio; Ramello, L.; Raso, G.; Retico, Alessandra; Santoro, Matteo; Sitta, M.; Stumbo, S.; Tangaro, Sabina; Tata, A.; Zanon, E

doi:10.1109/nssmic.2004.1462616

Cited by 22 publications

(22 citation statements)

References 3 publications

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“…Even though this global percentage was not optimal, it represents an interesting point for future investigation, since it contradicts other studies which concluded that microcalcification are a more predictable lesion than masses (36,54). We can conclude that, for the scenario 3, in distinguishing the type of mammographic lesion the best performance was obtained for scenario 3a by the use of k-NN classifier and for Random Forest classifier in scenario 3b.…”

Section: Chapter 6 Conclusion and Further Workcontrasting

confidence: 58%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

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More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method.In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis.In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings.Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix.For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests).The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS ® scale.The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications.The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques. VIII IX ResumoCada vez mais assistimos a um aumento global do número de métodos de apoio a decisão e diagnóstico assistido por computador, aplicados a diversas áreas da medicina.Na área de investigação do cancro da mama muitos são os trabalhos que têm sido desenvolvidos como segunda leitura de modo a reduzir o número de falsos positivos no diagnóstico.Neste estudo é apresentado um conjunto de técnicas de data mining que poderão ser aplicadas a um sistema de apoio à decisão na área do diagnóstico de cancro da mama.Esta abordagem tem por objetivo ajudar os clínicos na identificação de achados mamográficos como microcalcificações, massas e mesmo tecidos normais, de forma a evitar diagnósticos errados.Para isso, neste trabalho é usada uma base de dados fidedigna, de 410 imagens correspondentes a 115 pacientes, contendo análises prévias, realizadas por radiologistas, de microcalcificações, massas e tecidos considerados normais.Ao longo deste trabalho são utilizadas duas técnicas de extração de características, a matriz de coocorrência de níveis de cinza e a matriz de comprimento da linha de níveis de cinza.

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Section: Chapter 6 Conclusion and Further Workcontrasting

confidence: 58%

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Diz

Marreiros

2015

New Contributions in Information Systems and Technologies

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“…In the state-of-the-art, there are a number of works that make use of predictive models or radiomic analysis to identify and characterize breast neoplasms and the state of lymph node involvement [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

Comes

Forgia

Didonna

et al. 2021

Cancers

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Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.

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“…Magic 5 [9] uses distributed computing infrastructure (GRID) to increase computational speed and accessibility, and share distributed image databases and aims at diagnosing early detection of lung cancer.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Review on Computer Aided Detection and Diagnosis of Lung Cancer Nodules

Parveen¹,

Kavitha²

2012

IJCT

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In this paper, a attempt has been made to summarize some of the information about CAD and CADx for the purpose of early detection and diagnosis of lung cancer. Computer Aided Detection (CADe) and Computer Aided Diagnosis (CADx), are procedures in medical information that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, and Ultrasound diagnostics yield a great deal of information, which the radiologist has to analyze and evaluate comprehensively in a short time. Thus, the use of digital computers to assist practitioners and physicians in diagnosing diseases and to offer a rapid access to medical information gained importance. CAD systems help scan digital images, e.g. from Computed Tomography (CT), for typical appearances and to highlight conspicuous sections, such as focal areas of lung nodules.

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The MAGIC-5 project: medical applications on a grid infrastructure connection

Cited by 22 publications

References 3 publications

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Using Data Mining Techniques to Support Breast Cancer Diagnosis

Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs

A Review on Computer Aided Detection and Diagnosis of Lung Cancer Nodules

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