Machine Learning-Based Prediction of Invisible Intraprostatic Prostate Cancer Lesions on 68Ga-PSMA-11 PET/CT in Patients with Primary Prostate Cancer

Yi, Zhilong; Hu, Siqi; Lin, Xiaofeng; Zou, Qiong; Zou, Min-Hong; Zhang, Zhanlei; Xu, Lei; Jiang, Ningyi; Zhang, Yong

doi:10.21203/rs.3.rs-868542/v1

Cited by 4 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One such modality is Gallium-68 PSMA-11 PET-CT scans. 204 Recent studies demonstrate that PSMA PET-CT scans can significantly improve prostate cancer detection and treatment planning, are more accurate than conventional imaging with CT and bone scanning, 205 add value to MRI for diagnosis, 206 and may enable better prediction of preoperative pathological outcomes than MRI.207-209 There is an opportunity for AI to address shortcomings of these new imaging modalities, but due to its recent FDA approval, only a few published AI models 32,210,211 exist for prostate cancer detection using PSMA PET currently.…”

Section: Limitations Of This Studymentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

show abstract

Section: Limitations Of This Studymentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

show abstract

“…In the current study, we enrolled both PCa and non-PCa patients to comprehensively evaluate radiomics data from PSMA. Yi et al [26] constructed a random forest model developed by 68 Ga-PSMA-11 PETbased radiomics features proven to be useful for the accurate prediction of invisible intraprostatic lesions on 68 Ga-PSMA-11 PET in patients with primary PCa (AUC, 0.903). Their study differed from the current study in that we evaluated both negative and non-negative PSMA-PET image cases.…”

Section: Discussionmentioning

confidence: 99%

“…Second, it will be challenging to determine the value for negative or diffuse-pattern PET images. Recent studies [25,26] showed that radiomics features derived from 68 Ga-PSMA-11 PET images based on half-glandular segmentation were helpful for predicting invisible PCa lesions. Solari et al [33] developed radiomics models based on delineating the whole prostate gland and showed good performances for predicting the postoperative Gleason score in PCa patients.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of 68Ga-PSMA-11 PET/CT-based radiomics model to detect primary prostate cancer

Zang

Yang

et al. 2022

EJNMMI Res

View full text Add to dashboard Cite

Background This study aimed to develop a novel analytic approach based on a radiomics model derived from 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT for predicting intraprostatic lesions in patients with prostate cancer (PCa). Methods This retrospective study included consecutive patients with or without PCa who underwent surgery or biopsy after 68Ga-PSMA-11 PET/CT. A total of 944 radiomics features were extracted from the images. A radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm with tenfold cross-validation in the training set. PET/CT images for the test set were reviewed by experienced nuclear medicine radiologists. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for the model and radiologists’ results. The AUCs were compared. Results The total of 125 patients (86 PCa, 39 benign prostate disease [BPD]) included 87 (61 PCa, 26 BPD) in the training set and 38 (61 PCa, 26 BPD) in the test set. Nine features were selected to construct the radiomics model. The model score differed between PCa and BPD in the training and test sets (both P < 0.001). In the test set, the radiomics model performed better than the radiologists’ assessment (AUC, 0.85 [95% confidence interval 0.73, 0.97] vs. 0.63 [0.47, 0.79]; P = 0.036) and showed higher sensitivity (model vs radiologists, 0.84 [0.63, 0.95] vs. 0.74 [0.53, 0.88]; P = 0.002). Conclusion Radiomics analysis based on 68Ga-PSMA-11 PET may non-invasively predict intraprostatic lesions in patients with PCa.

show abstract

“…So far, studies applying radiomics analyses to PSMA PET data in prostate cancer demonstrated potential applications for detection, risk assessment, and prognosis at initial diagnosis (15)(16)(17). Only a few studies have evaluated the potential of PET radiomics for patient selection to 177 Lu-PSMA treatment (18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer

Gutsche,

Gülmüs,

Mottaghy

et al. 2023

Front. Nucl. Med.

View full text Add to dashboard Cite

ObjectiveThe treatment with 177Lutetium PSMA (177Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help to tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomics parameters extracted from pretreatment 68Ga-PSMA PET images for prediction of treatment response.MethodsForty-five mCRPC patients treated with 177Lu-PSMA-617 from two university hospital centers were retrospectively reviewed for this study. Radiomics features were extracted from the volumetric segmentations of metastases in the bone. A random forest model was trained and validated to predict treatment response based on age and conventionally used PET parameters, radiomics features, and combinations thereof. Further, overall survival was predicted by using the identified radiomics signature and compared to a Cox regression model based on age and PET parameters.ResultsThe machine learning model based on a combined radiomics signature of three features and patient age achieved an AUC of 0.82 in 5-fold cross validation and outperformed models based on age and PET parameters or radiomics features (AUC, 0.75 and 0.76, respectively). A Cox regression model based on this radiomics signature showed the best performance to predict the overall survival (C-index, 0.67).ConclusionOur results demonstrate that a machine learning model to predict response to 177Lu-PSMA treatment based on a combination of radiomics and patient age outperforms a model based on age and PET parameters. Moreover, the identified radiomics signature based on pretreatment 68Ga-PSMA PET images might be able to identify patients with an improved outcome and serve as a supportive tool in clinical decision making.

show abstract

Machine Learning-Based Prediction of Invisible Intraprostatic Prostate Cancer Lesions on 68Ga-PSMA-11 PET/CT in Patients with Primary Prostate Cancer

Cited by 4 publications

References 33 publications

A review of artificial intelligence in prostate cancer detection on imaging

A review of artificial intelligence in prostate cancer detection on imaging

Development and validation of 68Ga-PSMA-11 PET/CT-based radiomics model to detect primary prostate cancer

Multicentric 68Ga-PSMA PET radiomics for treatment response assessment of 177Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer

Contact Info

Product

Resources

About