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Ultrasonic tissue-type imaging (TTI) for planning treatment of prostate cancer

Feleppa, EJ; Ketterling, J; Porter, CR; Gillespie, J; Wuu, CS; Urban, S; Kalisz, A; Ennis, RD; Schiff, PB
Our research is intended to develop ultrasonic methods for characterizing cancerous prostate tissue and thereby to improve the effectiveness of biopsy guidance, therapy targeting, and treatment monitoring. We acquired radio frequency (RF) echo-signal data and clinical variables, e.g., PSA, during biopsy examinations. We computed spectra of the RF signals in each biopsied region, and trained neural network classifiers with over 3,000 sets of data using biopsy data as the gold standard. For imaging, a lookup table returned scores for cancer likelihood on a pixel-by-pixel basis from spectral-parameter and PSA values. Using ROC analyses, we compared classification performance of artificial neural networks (ANNs) to conventional classification with a leave-one-patient-out approach intended to minimize the chance of bias. Tissue-type images (TTIs) were compared to prostatectomy histology to further assess classification performance. ROC-curve areas were greater for ANNs than for the B-mode-based classification by more than 20%, e.g., 0.75 +/- 0.03 for neural-networks vs. 0.64 +/- 0.03 for B-mode LOSs. ANN sensitivity was 17% better than the sensitivity range of ultrasound-guided biopsies. TTIs showed tumors that were entirely unrecognized in conventional images and undetected during surgery. We are investigating TTIs for guiding prostate biopsies, and for planning radiation dose-escalation and tissue-sparing options, and monitoring prostate cancer
ISSN: 1996-756x
CID: 100762

The good news about giving bad news to patients

Farber, Neil J; Urban, Susan Y; Collier, Virginia U; Weiner, Joan; Polite, Ronald G; Davis, Elizabeth B; Boyer, E Gil
BACKGROUND: There are few data available on how physicians inform patients about bad news. We surveyed internists about how they convey this information. METHODS: We surveyed internists about their activities in giving bad news to patients. One set of questions was about activities for the emotional support of the patient (11 items), and the other was about activities for creating a supportive environment for delivering bad news (9 items). The impact of demographic factors on the performance of emotionally supportive items, environmentally supportive items, and on the number of minutes reportedly spent delivering news was analyzed by analysis of variance and multiple regression analysis. RESULTS: More than half of the internists reported that they always or frequently performed 10 of the 11 emotionally supportive items and 6 of the 9 environmentally supportive items while giving bad news to patients. The average time reportedly spent in giving bad news was 27 minutes. Although training in giving bad news had a significant impact on the number of emotionally supportive items reported (P <.05), only 25% of respondents had any previous training in this area. Being older, a woman, unmarried, and having a history of major illness were also associated with reporting a greater number of emotionally supportive activities. CONCLUSIONS: Internists report that they inform patients of bad news appropriately. Some deficiencies exist, specifically in discussing prognosis and referral of patients to support groups. Physician educational efforts should include discussion of prognosis with patients as well as the availability of support groups
PMID: 12472927
ISSN: 0884-8734
CID: 93991

Spectrum-analysis and neural networks for imaging to detect and treat prostate cancer

Feleppa, E J; Ennis, R D; Schiff, P B; Wuu, C S; Kalisz, A; Ketterling, J; Urban, S; Liu, T; Fair, W R; Porter, C R; Gillespie, J R
Conventional B-mode ultrasound currently is the standard means of imaging the prostate for guiding prostate biopsies and planning brachytherapy to treat prostate cancer. Yet B-mode images do not adequately display cancerous lesions of the prostate. Ultrasonic tissue-type imaging based on spectrum analysis of radiofrequency (rf) echo signals has shown promise for overcoming the limitations of B-mode imaging for visualizing prostate tumors. This method of tissue-type imaging utilizes nonlinear classifiers, such as neural networks, to classify tissue based on values of spectral parameter and clinical variables. Two- and three-dimensional images based on these methods demonstrate potential for guiding prostate biopsies and targeting radiotherapy of prostate cancer. Two-dimensional images are being generated in real time in ultrasound scanners used for real-time biopsy guidance and have been incorporated into commercial dosimetry software used for brachytherapy planning. Three-dimensional renderings show promise for depicting locations and volumes of cancer foci for disease evaluation to assist staging and treatment planning, and potentially for registration or fusion with CT images for targeting external-beam radiotherapy
PMID: 11958585
ISSN: 0161-7346
CID: 100721


Chapter by: Urban SY
in: Bellevue guide to outpatient medicine by Link N; Tanner M; Ofri D; Wasserman L [Eds]
London : BMJ, 2001
pp. 283-290
ISBN: 0727916807
CID: 3586

Breast disease

Chapter by: Urban SY
in: Bellevue guide to outpatient medicine by Link N; Tanner M; Ofri D; Wasserman L [Eds]
London : BMJ, 2001
pp. 55-67
ISBN: 0727916807
CID: 3587

Prostate imaging based on RF spectrum analysis and non-linear classifiers for guiding biopsies and targeting radiotherapy

Feleppa, E.J.; Ketterling, J.A.; Kalisz, A.; Urban, S.; Porter, C.R.; Gillespie, J.; Schiff, P.B.; Ennis, R.D.; Wuu, C.S.; Moul, J.W.; Sesterhenn, I.A.; Scardino, P.T.
Conventional B-mode ultrasound is the standard means of imaging the prostate for guiding prostate biopsies and planning radiotherapy (i.e., brachytherapy and external-beam radiation) of prostate cancer (CaP). Yet B-mode images essentially do not allow visualization of cancerous lesions of the prostate. Ultrasonic tissue-typing imaging based on spectrum analysis of radiofrequency (RF) echo signals has shown promise for overcoming the limitations of B-mode imaging in distinguishing cancerous from common forms of non-cancerous prostate tissue. Such tissue typing utilizes non-linear methods, such as nearest-neighbor and neural-network techniques, to classify tissues based on spectral-parameter and clinical-variable values. Our research seeks to develop imaging techniques based on these methods for the purpose of improving the guidance of prostate biopsies and the targeting of brachytherapy and external-beam radiotherapy of prostate cancer. Images based on these methods have been imported into real-time instrumentation for biopsy guidance and into commercial dose-planning software for real-time brachytherapy. Three-dimensional renderings show locations and volumes of cancer foci. These methods offer exciting possibilities for effective low-cost depiction of prostate cancer in real-time and off-line images. Real-time imaging showing cancerous regions of the prostate can be of value in directing biopsies, determining whether biopsy is warranted, assisting in clinical staging, targeting brachytherapy, planning conformal external-beam radiation procedures, and monitoring treatment
ISSN: 1996-756x
CID: 100801

Advanced ultrasonic tissue-typing and imaging based on radio-frequency spectrum analysis and neural-network classification for guidance of therapy and biopsy procedures

Chapter by: Feleppa EJ; Ketterling JA; Kalisz A; Urban S; Porter CR; Gillespie JW; Schiff PB; Ennis RD; Wuu CS; Fair WR
in: CARS 2001 : proceedings of the 15th international congress and exhibition, Berlin, June 27-30, 2001 by Lemke HU [Eds]
Amsterdam : Elsevier Science, 2001
pp. 333-337
ISBN: 044450866x
CID: 5094

Targeting and monitoring radiation therapy of prostate cancer using ultrasonic spectrum-analysis and neural-network classification for tissue-type imaging

Feleppa, E. J.; Kalisz, A.; Ketterling, J.; Urban, S.; Porter, C. R.; Schiff, P. B.; Ennis, R. D.; Wuu, C. S.; Liu, T.
ISSN: 0360-3016
CID: 101069