Biomedical subjects

E J Gamito

Publications and source records attributed to E J Gamito.

8 recordsLinked to original sources

PubMed2001

Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer.

OBJECTIVES: To develop an artificial neural network (ANN) model to predict lymph node (LN) spread in men with clinically localized prostate cancer and to describe a clinically useful method for interpreting the ANN's output scores. METHODS: A simple, feed-forward ANN was trained and validated using clinical and pathologic data from two institutions (n = 6135 and n = 319). The clinical stage, biopsy Gleason sum, and prostate-specific antigen level were the input parameters and the presence or absence of LN spread was the output parameter. Patients with similar ANN outputs were grouped and assumed to be part of a cohort. The prevalence of LN spread for each of these patient cohorts was plotted against the range of ANN outputs to create a risk curve. RESULTS: The area under the receiver operating characteristic curve for the first and second validation data sets was 0.81 and 0.77, respectively. At an ANN output cutoff of 0.3, the sensitivity achieved for each validation set was 63.8% and 44.4%; the specificity was 81.5% and 81.3%; the positive predictive value was 13.6% and 6.5%; and the negative predictive value was 98.0% and 98.1%, respectively. The risk curve showed a nearly linear increase (best fit R(2) = 0.972) in the prevalence of LN spread with increases in raw ANN output. CONCLUSIONS: The ANN's performance on the two validation data sets suggests a role for ANNs in the accurate clinical staging of patients with prostate cancer. The risk curve provides a clinically useful tool that can be used to give patients a realistic assessment of their risk of LN spread.

False Negative Reactions↗

PubMed2001

Predictive modeling techniques in prostate cancer.

A number of new predictive modeling techniques have emerged in the past several years. These methods can be used independently or in combination with traditional modeling techniques to produce useful tools for the management of prostate cancer. Investigators should be aware of these techniques and avail themselves of their potentially useful properties. This review outlines selected predictive methods that can be used to develop models that may be useful to patients and clinicians for prostate cancer management.

Humans↗

PubMed2001

Use of artificial neural networks in prostate cancer.

Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research.

Humans↗

PubMed2001

Artificial neural network model to predict biochemical failure after radical prostatectomy.

BACKGROUND: Biochemical failure, defined here as a rise in the serum prostate specific antigen (PSA) concentration to >0.3 ng/mL or the initiation of adjuvant therapy, is thought to be an adverse prognostic factor for men who undergo radical prostatectomy (RP) as definitive treatment for clinically localized cancer of the prostate (CAP). We have developed an artificial neural network (ANN) to predict biochemical failure that may benefit clinicians and patients choosing among the definitive treatment options for CAP. MATERIALS AND METHODS: Clinical and pathologic data from 196 patients who had undergone RP at one institution between 1988 and 1999 were utilized. Twenty-one records were deleted because of missing outcome, Gleason sum, PSA, or clinical stage data. The variables from the 175 remaining records were analyzed for input variable selection using principal component analysis, decision tree analysis, and stepped logistic regression. The selected variables were age, PSA, primary and secondary Gleason grade, and Gleason sum. The records were randomized and split into three bootstrap training and validation sets of 140 records (80%) and 35 records (20%), respectively. RESULTS: Forty-four percent of the patients suffered biochemical failure. The average duration of follow up was 2.5 years (range 0-11.5 years). Forty-two percent of the patients had pathologic evidence of non-organ-confined disease. The average area under the receiver operator characteristic (ROC) curve for the validation sets was 0.75 +/- 0.07. The ANN with the highest area under the ROC curve (0.80) was used for prediction and had a sensitivity of 0.74, a specificity of 0.78, a positive predictive value of 0.71, and a negative predictive value of 0.81. CONCLUSION: These results suggest that ANN models can predict PSA failure using readily available preoperative variables. Such predictive models may offer assistance to patients and physicians deciding on definitive therapy for CaP.

Adjuvants, Pharmaceutic↗

PubMed2001

Genetic adaptive neural network to predict biochemical failure after radical prostatectomy: a multi-institutional study.

BACKGROUND AND PURPOSE: Despite many new procedures, radical prostatectomy remains one of the commonest methods of treating clinically localized prostate cancer. Both from the physician's and the patient's point of view, it is important to have objective estimation of the likelihood of recurrence, which forms the foundation for treatment selection for an individual patient. Currently, it is difficult to predict the probability of biochemical recurrence (rising serum prostate specific antigen [PSA] concentration) in an individual patient, and approximately 30% of the patients do experience recurrence. Tools predicting the recurrence will be of immense practical utility in the treatment selection and planning follow up. We have utilized preoperative parameters through a computer based genetic adaptive neural network model to predict recurrence in such patients, which can help primary care physicians and urologists in making management recommendations. PATIENTS AND METHODS: Fourteen hundred patients who underwent radical prostatectomy at participating institutions form the subjects of this study. Demographic data such as age, race, preoperative PSA, systemic biopsy based staging and Gleason scores were used to construct a neural network model. This model simulated the functioning of a trained human mind and learned from the database. Once trained, it was used to predict the outcomes in new patients. RESULTS: The patients in this comprehensive database were representative of the average prostate cancer patients as seen in USA. Their mean age was 68.4 years, the mean PSA concentration before surgery was 11.6 ng/mL, and 67% patients had a Gleason sum of 5 to 7. The mean length of follow-up was 41.5 months. Eighty percent of the cancers were clinical stage T2 and 5% T3. In our series, 64% of patients had pathologically organ-confined cancer, 33% positive margins, and 14% had seminal vesicle invasion. Lymph node positive patients were not included in this series. Progression as judged by serum PSA was noted in 30.6%. With entry of a few routinely used parameters, the model could correctly predict recurrence in 76% of the patients in the validation set. The area under the curve was 0.831. The sensitivity was 85%, the specificity 74%, the positive predictive value 77%, and the negative predictive value of 83%. CONCLUSION: It was possible to predict PSA recurrence with a high accuracy (76%). Physicians desiring objective treatment counseling can use this model, and significant cost savings are anticipated because of appropriate treatment selection and patient-specific follow-up protocols. This technology can be extended to other treatments such as watchful waiting, external-beam radiation, and brachytherapy.

Aged↗

PubMed2001

Prostate cancer: risk assessment and diagnostic approaches.

The successful treatment of prostate cancer relies on detection of the disease at its earliest stages. Although prostate-specific antigen (PSA)-based screening has been a significant advance in the early diagnosis of prostate cancer, identifying specific genetic alterations in a given family or patient will allow more appropriate screening for early disease. Mapping and identification of specific prostate cancer susceptibility genes is slowly becoming a reality. Other prostate cancer risks include a family history, race, and possibly serum markers such as insulin-like growth factor-I (IGF-I). Once a high-risk man is identified, transrectal ultrasound (TRUS)-guided biopsies are the standard to diagnose prostate cancer. Although TRUS is an advance over traditional digitally directed biopsies, it represents a random sampling of the prostate since most lesions cannot be visualized. Newer modalities such as ultrasound contrast agents, pattern recognition, and artificial neural networks (ANNs), applied to TRUS images, may improve diagnostic accuracy. If a man at risk for prostate cancer has undergone a negative TRUS biopsy, the decision for the need for additional biopsies is problematic. Use of PSA derivatives such as free and total PSA and the initial biopsy abnormalities such as atypia or high-grade prostatic intraepithelial neoplasia may define those patients in need of follow-up biopsy.

Journal Article↗

PubMed2000

The use of artificial intelligence technology to predict lymph node spread in men with clinically localized prostate carcinoma.

BACKGROUND: The current study assesses artificial intelligence methods to identify prostate carcinoma patients at low risk for lymph node spread. If patients can be assigned accurately to a low risk group, unnecessary lymph node dissections can be avoided, thereby reducing morbidity and costs. METHODS: A rule-derivation technology for simple decision-tree analysis was trained and validated using patient data from a large database (4,133 patients) to derive low risk cutoff values for Gleason sum and prostate specific antigen (PSA) level. An empiric analysis was used to derive a low risk cutoff value for clinical TNM stage. These cutoff values then were applied to 2 additional, smaller databases (227 and 330 patients, respectively) from separate institutions. RESULTS: The decision-tree protocol derived cutoff values of < or = 6 for Gleason sum and < or = 10.6 ng/mL for PSA. The empiric analysis yielded a clinical TNM stage low risk cutoff value of < or = T2a. When these cutoff values were applied to the larger database, 44% of patients were classified as being at low risk for lymph node metastases (0.8% false-negative rate). When the same cutoff values were applied to the smaller databases, between 11 and 43% of patients were classified as low risk with a false-negative rate of between 0.0 and 0.7%. CONCLUSIONS: The results of the current study indicate that a population of prostate carcinoma patients at low risk for lymph node metastases can be identified accurately using a simple decision algorithm that considers preoperative PSA, Gleason sum, and clinical TNM stage. The risk of lymph node metastases in these patients is < or = 1%; therefore, pelvic lymph node dissection may be avoided safely. The implications of these findings in surgical and nonsurgical treatment are significant.

Acid Phosphatase↗

PubMed2000

Use of artificial neural networks in the clinical staging of prostate cancer: implications for prostate brachytherapy.

PURPOSE: This review describes two studies to evaluate artificial neural networks (ANNs) in prostate cancer staging. In the first study, an ANN was trained to identify prostate cancer patients at low risk of lymph node spread (LNS). The second study evaluated an ANN to predict capsular penetration (CP) in men with clinically localized prostate cancer. An accurate assessment of lymph node status will help identify those brachytherapy patients in whom lymphadenectomy can be avoided. The accurate prediction of CP can help determine the appropriateness of brachytherapy as a treatment option. MATERIALS AND METHODS: An ANN to predict LNS was trained and tested using a database from one institution (n = 4,133) and validated using two databases (n = 330 and n = 227) from different institutions. The clinical variables used were clinical stage (cTNM), Gleason sum, and prostate-specific antigen concentration (PSA). The ANN to predict CP was trained and validated with data from a single institution (n = 409). The variables used were age, race, PSA, PSA velocity, Gleason sum, and cTNM. RESULTS: The LNS ANN was able classify 76%, 75%, and 30% of the patients in each database as being at low risk of LNS with 98% accuracy. The CP ANN correctly identified CP in 25 (84%) of patients and produced 5 (16%) false-negative predictions. CONCLUSIONS: These preliminary results suggest that ANNs can be useful in staging prostate cancer. If sufficiently accurate ANNs can be developed and tested, they have the potential to increase the accuracy of clinical staging and thus improve treatment decisions.

Biopsy↗