The Concept of the Index Lesion

2014 ◽  
pp. 9-17 ◽  
Author(s):  
Ian A. Donaldson ◽  
Mark Emberton ◽  
Alex Freeman ◽  
Hashim U. Ahmed
Keyword(s):  
Index Lesion

scholarly journals Correlation of an Index-Lesion-Based SPECT Dosimetry Method with Mean Tumor Dose and Clinical Outcome after 177Lu-PSMA-617 Radioligand Therapy

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 428
Author(s):  
Friederike Völter ◽  
Lena Mittlmeier ◽  
Astrid Gosewisch ◽  
Julia Brosch-Lenz ◽  
Franz Josef Gildehaus ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Single Photon ◽  
Photon Emission ◽  
Whole Body ◽  
Biochemical Response ◽  
Emission Computed Tomography ◽  
Radioligand Therapy ◽  
Index Lesion ◽  
Recist 1.1 ◽  
Psma Pet

Background: Dosimetry can tailor prostate-specific membrane-antigen-targeted radioligand therapy (PSMA-RLT) for metastatic castration-resistant prostate cancer (mCRPC). However, whole-body tumor dosimetry is challenging in patients with a high tumor burden. We evaluate a simplified index-lesion-based single-photon emission computed tomography (SPECT) dosimetry method in correlation with clinical outcome. Methods: 30 mCRPC patients were included (median 71 years). The dosimetry was performed for the first cycle using quantitative 177Lu-SPECT. The response was evaluated using RECIST 1.1 and PERCIST criteria, as well as changes in PSMA-positive tumor volume (PSMA-TV) in post-therapy PSMA-PET and biochemical response according to PSA changes after two RLT cycles. Results: Mean tumor doses as well as index-lesion doses were significantly higher in PERCIST responders compared to non-responders (10.2 ± 12.0 Gy/GBq vs. 4.0 ± 2.9 Gy/GBq, p = 0.03 and 13.7 ± 14.2 Gy/GBq vs. 5.9 ± 4.4 Gy/GBq, p = 0.04, respectively). No significant differences in mean tumor and index lesion doses were observed between responders and non-responders according to RECIST 1.1, PSMA-TV, and biochemical response criteria. Conclusion: Compared to mean tumor doses on a patient level, single index-lesion-based SPECT dosimetry correlates equally well with the response to PSMA-RLT according to PERCIST criteria and may represent a fast and feasible dosimetry approach for clinical routine.

scholarly journals MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

2021 ◽  
Author(s):  
Renato Cuocolo ◽  
Arnaldo Stanzione ◽  
Riccardo Faletti ◽  
Marco Gatti ◽  
Giorgio Calleris ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Index Lesion ◽  
Pairwise Correlation ◽  
Experienced Radiologist ◽  
Expert Radiologist ◽  
External Test ◽  
Extraprostatic Extension

Abstract Objectives To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions. Methods Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE. Results In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02). Conclusions A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task. Key Points • Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists. • A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets. • The performance of the algorithm was not significantly different from that of an experienced radiologist.

Assessing the need for systematic biopsies in addition to targeted biopsies according to the volume of the index lesion diagnosed at mpmri. Results from a large multi-institutional database

2021 ◽  
Vol 79 ◽  
pp. S1414-S1415
Author(s):  
F. Pellegrino ◽  
E. Mazzone ◽  
A. Stabile ◽  
G. Sorce ◽  
L. Nocera ◽  
...  
Keyword(s):  
Index Lesion ◽  
Targeted Biopsies

Identifying the Index Lesion

2012 ◽  
pp. 73-80
Author(s):  
Markos Karavitakis ◽  
Mark Emberton ◽  
Hashim Uddin Ahmed
Keyword(s):  
Index Lesion

scholarly journals MP11-02 USE OF THE PI-RADS v2 DEFINED RELATIONSHIP OF AN INDEX LESION AND PROSTATIC CAPSULE IMPROVES THE STAGING ACCURACY OF MRI

2021 ◽  
Vol 206 (Supplement 3) ◽  
Author(s):  
Zachary Feuer ◽  
Ezequiel Becher ◽  
Angela Tong ◽  
Richard Huang ◽  
James S. Wysock ◽  
...  
Keyword(s):  
Index Lesion ◽  
Prostatic Capsule ◽  
Staging Accuracy ◽  
Relationship Of

scholarly journals Synchronous multiple primary non-small cell lung cancer or intrapulmonary metastasis from an index lesion: a dilemma—case report

2019 ◽  
Vol 1 ◽  
pp. 24-24
Author(s):  
Soumya Guha ◽  
Roberto Cascone ◽  
Annalisa Carlucci ◽  
Panagiota Kitsanta ◽  
Laura Socci
Keyword(s):  
Lung Cancer ◽  
Case Report ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Index Lesion ◽  
Multiple Primary

scholarly journals Nomogram including maximum core length at biopsy and index lesion size at mpMRI best predicts the extra-capsular extension of prostate cancer at whole-mount sections of radical prostatectomies

2020 ◽  
Vol 19 ◽  
pp. e613-e614
Author(s):  
F. Sanguedolce ◽  
J.D. Subiela Henriquez ◽  
L. Granados Palacio ◽  
J. Hernandez Mancera ◽  
M.J. Martinez Barcina ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lesion Size ◽  
Index Lesion ◽  
Core Length ◽  
Whole Mount
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