Nikolaos I. Papandrianos

An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps

BACKGROUND There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reported work is on the development of an integrated risk prediction model using a two-level fuzzy cognitive map (FCM) model. The proposed model combines the results of the initial screening mammogram of the given woman with her demographic risk factors to predict the post-screening risk of developing BC. METHODS The level-1 FCM models the demographic risk profile. A nonlinear Hebbian learning algorithm is used to train this model and thus to help on predicting the BC risk grade based on demographic risk factors identified by domain experts. The risk grades estimated by the proposed model are validated using two standard BC risk assessment models viz. Gail and Tyrer-Cuzick. The level-2 FCM models the features of the screening mammogram concerning normal, benign and malignant cases. The data driven Hebbian learning algorithm (DDNHL) is used to train this model in order to predict the BC risk grade based on these mammographic image features. An overall risk grade is calculated by combining the outcomes of these two FCMs. RESULTS The main limitation of the Gail model of underestimating the risk level of women with strong family history is overcome by the proposed model. IBIS is a hard computing tool based on the Tyrer-Cuzick model that is comprehensive enough in covering a wide range of demographic risk factors including family history, but it generates results in terms of numeric risk score based on predefined formulae. Thus the outcome is difficult to interpret by naive users. Besides these models are based only on the demographic details and do not take into account the findings of the screening mammogram. The proposed integrated model overcomes the above described limitations of the existing models and predicts the risk level in terms of qualitative grades. The predictions of the proposed NHL-FCM model comply with the Tyrer-Cuzick model for 36 out of 40 patient cases. With respect to tumor grading, the overall classification accuracy of DDNHL-FCM using 70 real mammogram screening images is 94.3%. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines. CONCLUSION In the perspective of clinical oncologists, this is a comprehensive front-end medical decision support system that assists them in efficiently assessing the expected post-screening BC risk level of the given individual and hence prescribing individualized preventive interventions and more intensive surveillance for high risk women.

A risk management model for familial breast cancer

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC.

Fuzzy Cognitive Map Based Approach for Assessing Pulmonary Infections

The decision making problem of predicting infectious diseases is a complex process, because of the numerous elements/parameters (such as symptoms, signs, physical examination, laboratory tests, cultures, chest x-rays, e.t.c.) involved in its operation, and a permanent attention is demanded. The knowledge of physicians according to the physical examination and clinical measurements is the main point to succeed a diagnosis and monitor patient status. In this paper, the Fuzzy Cognitive Mapping approach is investigating to handle with the problem of pulmonary infections during the patient admission into the hospital or in Intensive Care Unit (ICU). This is the first step in the development of a decision support system for the process of infectious diseases prediction.

Fuzzy Cognitive Map based decision support system for thyroid diagnosis management

Knowledge-based systems are the most common type of artificial intelligence in medicine systems in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Fuzzy cognitive map (FCM) is a knowledge based modeling methodology based on exploiting knowledge and experience from experts. It can handle uncertainty and can be constructed basely by expertspsila knowledge.

Atypical bilateral stress fractures of the femoral shaft diagnosed by bone scintigraphy in a woman with osteoporosis.

Recent case series have identified the presence of atypical insufficiency fractures at the diaphyseal femur of osteoporotic patients, which are possibly related to the long-term use of biphosphonates. We present images of a 72-year-old woman with a history of colon cancer and osteoporosis referred for bone scintigraphy because of bilateral thigh pain. No trauma or intense exercise was reported. Bone scan revealed bilateral femoral shaft stress fractures, which were confirmed by plain radiographs. In oncologic patients with osteoporosis referred for bone scintigraphy, atypical stress fractures should be included in the differential diagnosis of focal findings in the diaphyseal femur.

Tc-99m Depreotide SPECT/CT depicts myocardial involvement in a case of thrombotic thrombocytopenic purpura.

We report a case of thrombotic thrombocytopenic purpura (TTP) with cardiac involvement, imaged with Tc-99m depreotide. A 56-year-old man presented with fever, hematuria, and chest pain. Laboratory findings (angiopathic hemolytic anemia, thrombocytopenia, and uremia) were suggestive of TTP. Cardiac enzymes were elevated and diffuse left ventricular hypokinesis was demonstrated by echocardiography. Serum rheumatologic and virologic analysis were negative. A Tc-99m depreotide SPECT/CT study showed diffuse uptake in the myocardium, indicating inflammatory reaction to thrombotic/hemorrhagic myocardial damage. We suggest that Tc-99m depreotide imaging may reveal myocardial involvement in TTP; this could prompt further investigation for potential applications in myocarditis of other etiologies.

Technetium-99m depreotide imaging by single photon emission tomography/low resolution computed tomography in malignant lymphomas: comparison with gallium-67 citrate

ObjectivePrevious studies have demonstrated the feasibility of targeting lymphoma lesions with somatostatin receptor binding agents, mainly with In-111-pentetreotide. In the present work another somatostatin analog, Tc-99m depreotide, is investigated.MethodsOne-hundred and six patients, 47 with Hodgkin’s (HL) and 59 with various types of non-Hodgkin’s lymphoma (NHL), were imaged with both Tc-99m depreotide and Ga-67 citrate. Planar whole-body and single photon emission tomography/low resolution computerized tomography (SPECT/CT) images were obtained. A total of 142 examinations were undertaken at different phases of the disease. Depreotide and gallium findings were compared visually and semi-quantitatively, with reference to the results of conventional work-up and the patients’ follow-up data.ResultsIn most HL, intermediate- and low-grade B-cell, as well as in T-cell NHL, depreotide depicted more lesions than Ga-67 and/or exhibited higher tumor uptake. The opposite was true in aggressive B-cell NHL. However, there were notable exceptions in all lymphoma subtypes. During initial staging, 93.3% of affected lymph nodes above the diaphragm, 100% of inguinal nodes and all cases with splenic infiltration were detected by depreotide. On the basis of depreotide findings, 32% of patients with early-stage HL were upstaged. However, advanced HL and NHL cases were frequently downstaged, due to low sensitivity for abdominal lymph node (22.7%), liver (45.5%) and bone marrow involvement (36.4%). Post-therapy, depreotide detected 94.7% of cases with refractory disease or recurrence. Its overall specificity was moderate (57.1%). Rebound thymic hyperplasia, various inflammatory processes and sites of unspecific uptake were the commonest causes of false positive findings. The combination of depreotide and gallium enhanced sensitivity (100%), while various false positive results of either agent could be avoided.ConclusionExcept perhaps for early-stage HL, Tc-99m depreotide as a stand-alone imaging modality has limited value for the initial staging of lymphomas. Post-therapy, however, depreotide scintigraphy seems useful in the evaluation of certain anatomic areas, particularly in non-aggressive lymphoma types. The combination with Ga-67 potentially enhances sensitivity and specificity. If fluorodeoxyglucose positron emission tomography is not available or in case of certain indolent lymphoma types, Tc-99m depreotide may have a role as an adjunct to conventional imaging procedures.