ONCOLOGY Vol 17 No 10

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Emerging Technology in Cancer Treatment: Radiotherapy Modalities

October 1st 2003
Article

This is a period of rapid developments in radiotherapy for malignantdisease. New methods of targeting tumors with computed tomography(CT) virtual simulation, magnetic resonance imaging (MRI), andpositron-emission tomography (PET) fusion provide the clinician withinformation heretofore unknown. Linear accelerators (linacs) withmultileaf collimation (MLC) have replaced lead-alloy blocks. Indeed,new attachments to the linacs allow small, pencil beams of radiation tobe emitted as the linac gantry rotates around the patient, conforming tothree-dimensional (3D) targets as never before. Planning for these deliverysystems now takes the form of "inverse planning," with CT informationused to map targets and the structures to be avoided. In thearea of brachytherapy, techniques utilizing the 3D information providedby the new imaging modalities have been perfected. Permanentseed prostate implants and high-dose-rate (HDR) irradiation techniquestargeting bronchial, head and neck, biliary, gynecologic, and otheranatomic targets are now commonplace radiotherapy tools. CT-guidedpermanent seed implants are being investigated, and a new method oftreating early breast cancer with HDR brachytherapy via a ballooncatheter placed in the lumpectomized cavity is coming to the forefront.Newer modalities for the treatment of malignant and benign diseaseusing stereotactic systems and body radiosurgery are being developed.Targeted radionuclides using microspheres that contain radioemittersand other monoclonal antibody systems tagged with radioemitters havebeen recently approved for use by the Food and Drug Administration.


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Combining Artificial Neural Networks and Transrectal Ultrasound in the Diagnosis of Prostate Cancer

October 1st 2003
Article

Arguably the most important step in the prognosis of prostate canceris early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the UnitedStates, resulting in the detection of 200,000 new cases per year. Unfortunately,the urologist's ability to diagnose prostate cancer has not keptpace with therapeutic advances; currently, many men are facing theneed for prostate biopsy with the likelihood that the result will beinconclusive. This paper will focus on the tools available to assist theclinician in predicting the outcome of the prostate needle biopsy. We willexamine the use of "machine learning" models (artificial intelligence),in the form of artificial neural networks (ANNs), to predict prostatebiopsy outcomes using prebiopsy variables. Currently, six validatedpredictive models are available. Of these, five are machine learningmodels, and one is based on logistic regression. The role of ANNs inproviding valuable predictive models to be used in conjunction withTRUS appears promising. In the few studies that have comparedmachine learning to traditional statistical methods, ANN and logisticregression appear to function equivalently when predicting biopsyoutcome. With the introduction of more complex prebiopsy variables,ANNs are in a commanding position for use in predictive models. Easyand immediate physician access to these models will be imperative iftheir full potential is to be realized.