Tissue-characterization of the prostate using radio frequency ultrasonic signals.
← Research streamA method/system processing radio-frequency ultrasonic echo signals was evaluated in a first clinical study of 33 patients with local prostatic carcinoma (histology after radical prostatectomy as reference). Using a 50% malignancy-probability threshold, the re…
Study snapshot
Abstract
In this paper, we will present a complete method and system for the detection of prostatic carcinoma, providing color-coded images of the estimated probability of malignancy by processing radio-frequency ultrasonic echo signals. For this, a hardware setup based on a conventional diagnostic sonograph was realized. The image-processing software works on ultrasound images automatically segmented into regions of about 3x3.5 mm. System-dependent effects, as well as tissue attenuation, were measured and compensated for. Tissue-characterisation parameters, which have been used successfully by other authors, were calculated for each segment. To demonstrate the methods of selection of relevant parameters and comparison of different classifiers, a first clinical study using data of 33 patients with local prostatic carcinoma was performed. For these patients, location and extent of the carcinoma were known from histological findings after radical prostatectomy. Classifiers investigated during the study were: the linear and quadratic Bayes classifier, a nearest neighbor classifier, and several classifiers based on Kohonen-maps. The best classifier was used to calculate color-coded result images. Applying a threshold of 50% to the estimated probability of malignancy, produced the encouraging results of 82 and 88% for sensitivity and specificity, respectively.
AI evidence extraction
Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.
Main findings
A method/system processing radio-frequency ultrasonic echo signals was evaluated in a first clinical study of 33 patients with local prostatic carcinoma (histology after radical prostatectomy as reference). Using a 50% malignancy-probability threshold, the reported sensitivity was 82% and specificity was 88%.
Outcomes measured
- Detection/classification of prostatic carcinoma (probability of malignancy images)
- Sensitivity
- Specificity
Limitations
- First clinical study
- Small sample size (33 patients)
- Only patients with known local prostatic carcinoma were included (as described)
View raw extracted JSON
{
"study_type": "other",
"exposure": {
"band": null,
"source": "diagnostic ultrasound (radio-frequency ultrasonic echo signals)",
"frequency_mhz": null,
"sar_wkg": null,
"duration": null
},
"population": "Patients with local prostatic carcinoma",
"sample_size": 33,
"outcomes": [
"Detection/classification of prostatic carcinoma (probability of malignancy images)",
"Sensitivity",
"Specificity"
],
"main_findings": "A method/system processing radio-frequency ultrasonic echo signals was evaluated in a first clinical study of 33 patients with local prostatic carcinoma (histology after radical prostatectomy as reference). Using a 50% malignancy-probability threshold, the reported sensitivity was 82% and specificity was 88%.",
"effect_direction": "unclear",
"limitations": [
"First clinical study",
"Small sample size (33 patients)",
"Only patients with known local prostatic carcinoma were included (as described)"
],
"evidence_strength": "low",
"confidence": 0.74,
"peer_reviewed_likely": "yes",
"keywords": [
"prostate",
"prostatic carcinoma",
"tissue characterization",
"radio-frequency ultrasound",
"ultrasonic echo signals",
"Bayes classifier",
"nearest neighbor",
"Kohonen maps",
"sensitivity",
"specificity"
],
"suggested_hubs": []
}AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.
Identifiers and source
DOI: 10.1109/58.741523
PubMed: 18238406