Emily Menendez

GU Oncology Now

The utilization of total tumor volume (TTV) and whole-body mean standardized uptake value (SUVmean) on 177Lu-PSMA single-photon emission computed tomography/computed tomography (SPECT/CT; Lu-SPECT) can predict outcomes in patients with metastatic castration-resistant prostate cancer (mCRPC) who are receiving 177Lu-PSMA therapy. As TTV is a lengthy process, <a href="https://www.xcdsystem.com/snmmi/program/oZQNYYR/index.cfm?pgid=861" target="_blank" rel="noopener">researchers have examined the efficacy of utilizing an algorithm developed by deep learning</a> to automatically generate TTV on Lu-SPECT.A total of 95 177Lu-PSMA therapy SPECT/CT scans from 36 patients with mCRPC were gathered from the LuPIN trial, which analyzed the safety and efficacy of 177Lu-PSMA 617 and idronoxil (NOX66) in patients with end-stage mCRPC. Each patient was administered 1 to 6 cycles of therapy, with each cycle occurring every 6 weeks. The algorithm segmented all volumes of interest, with a 3-SUV threshold. The proposed method utilized CT-based, deep learning-generated normal organs for automatic removal of physiologic uptake on Lu-SPECT.The average TTV for the proposed method was 927 mL compared with an average Gleason score (GS) TTV of 847 mL, which was not deemed significantly different (P=.054). The average absolute difference in TTV between the proposed method and GS was 110 mL.The average SUVmean of the proposed method was 8.60, and the average SUVmean of GS was 8.96. This difference was deemed significant (P=.03). The average relative error in SUVmean was 4.7%, and the median relative error was 1.4%.The proposed method of algorithm-based TTV produced comparable TTV and whole-body SUVmean levels. A small but statistically significant difference in SUVmean was noted between the proposed method and GS, but the clinical significance of this difference is unclear.A tool using the proposed method may prove to be beneficial by increasing efficacy and reproducibility when generating TTV and whole-body SUVmean for the assessment of 177Lu-PSMA therapy. Further research is needed to assess the actual amount of time saved when utilizing automatic physiological uptake removal.

Utilizing Deep Learning to Automate TTV Generation on 177Lu-PSMA SPECT/CT

SNMMI 2023

As TTV is a lengthy process, researchers examined the efficacy of utilizing an algorithm developed by deep learning to automatically generate TTV on Lu-SPECT.

Deep Learning Automates TTV Generation on 177Lu-PSMA SPECT/CT

About Us

Contributors and Partners

Editorial Policy

Advertising

eNewsletters

Calendar

Contact Us

Contribute

MashupMD

Cancer Nursing Today

Urban Health Today

Blood Cancers Today

DocWire News

Knowledge Hubs

Conference Coverage

Roundtables

The Uromigos

Uromigos Live 2024

AUC3

Localized

mHSPC/mCSPC

CRPC

Diagnostics

Prostate Cancer

Localized Renal Cell Carcinoma

Advanced Renal Cell Carcinoma

nccRCC

Renal Cell Carcinoma

Non-Muscle Invasive Urothelial Carcinoma

Muscle Invasive Urothelial Carcinoma

Advanced Urothelial Carcinoma

Upper Tract Urothelial Carcinoma

Urothelial Carcinoma

Testicular, Penile, and Rare Cancers

Video Insights

Advancements in Oncology

The Oncology Brothers

Expert Interviews

Journal

Subscribe