Analyzing the potential of radiomics features and radiomics signature from pretherapeutic PSMA-PET-CT scans and clinical data for prediction of overall survival when treated with Lu[177]-PSMA

Moazemi, Sobhan; Erle, A; Essler, Markus; Bundschuh, RA

doi:10.1055/s-0041-1726854

“…Moazemi et al. ( 40 ) assessed the predictive values of RFs extracted from pretreatment 68Ga-PSMA PET-CT and baseline clinical parameters for OS in patients undergoing 177Lu-PSMA therapy. They found that SUV min , SUV mean , and kurtosis as RFs and baseline values of hemoglobin and C-reactive protein (CRP), and Eastern Cooperative Oncology Group (ECOG) performance status scale as clinical parameters had a significant impact on OS.…”

Section: Discussionmentioning

confidence: 99%

Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA

Assadi

¹

,

Manafi‐Farid

²

,

Jafari

³

et al. 2022

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IntroductionThis study was conducted to evaluate the predictive values of volumetric parameters and radiomic features (RFs) extracted from pretreatment 68Ga-PSMA PET and baseline clinical parameters in response to 177Lu-PSMA therapy.Materials and methodsIn this retrospective multicenter study, mCRPC patients undergoing 177Lu-PSMA therapy were enrolled. According to the outcome of therapy, the patients were classified into two groups including positive biochemical response (BCR) (≥ 50% reduction in the serum PSA value) and negative BCR (< 50%). Sixty-five RFs, eight volumetric parameters, and also seventeen clinical parameters were evaluated for the prediction of BCR. In addition, the impact of such parameters on overall survival (OS) was evaluated.Results33 prostate cancer patients with a median age of 69 years (range: 49-89) were enrolled. BCR was observed in 22 cases (66%), and 16 cases (48.5%) died during the follow-up time. The results of Spearman correlation test indicated a significant relationship between BCR and treatment cycle, administered dose, HISTO energy, GLCM entropy, and GLZLM LZLGE (p<0.05). In addition, according to the Mann-Whitney U test, age, cycle, dose, GLCM entropy, and GLZLM LZLGE were significantly different between BCR and non BCR patients (p<0.05). According to the ROC curve analysis for feature selection for prediction of BCR, GLCM entropy, age, treatment cycle, and administered dose showed acceptable results (p<0.05). According to SVM for assessing the best model for prediction of response to therapy, GLCM entropy alone showed the highest predictive performance in treatment planning. For the entire cohort, the Kaplan-Meier test revealed a median OS of 21 months (95% CI: 12.12-29.88). The median OS was estimated at 26 months (95% CI: 17.43-34.56) for BCR patients and 13 months (95% CI: 9.18-16.81) for non BCR patients. Among all variables included in the Kaplan Meier, the only response to therapy was statistically significant (p=0.01).ConclusionThis exploratory study showed that the heterogeneity parameter of pretreatment 68Ga-PSMA PET images might be a potential predictive value for response to 177Lu-PSMA therapy in mCRPC; however, further prospective studies need to be carried out to verify these findings.

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“…In another single-center cohort of 83 patients, Moazemi et al investigated deep learning applications in pre-therapeutic 68 Ga-PSMA PET/CT for lesion detection and 177 Lu-PSMA therapy guidance in metastatic prostate cancer, showing high diagnostic accuracy 58 . Radiomic features (SUV min , SUV correlation, CT min, CT busyness and CT coarseness) in 68 Ga-PSMA PET/CT and clinical parameters such as Alp1 and Gleason score yielded strong correlations with changes in prostate-specific antigen (PSA) to predict outcome 58 59 . This finding also points in the direction of integrated diagnostics where the integration of multi-source diagnostic data from medical imaging, pathology, liquid biopsy, and clinical findings is analyzed to achieve optimized diagnostic accuracy in evidence-based clinical decision guidance.…”

Section: Applications Of Ai In Oncological Hybrid Imagingmentioning

confidence: 99%

Artificial Intelligence in Oncological Hybrid Imaging

Feuerecker,

Heimer,

Geyer

et al. 2023

Nuklearmedizin

1

0

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Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points:

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“…In another single-center cohort of 83 patients, Moazemi et al investigated deep learning applications in pre-therapeutic 68 Ga-PSMA PET/CT for lesion detection and 177 Lu-PSMA therapy guidance in metastatic prostate cancer, showing high diagnostic accuracy [58]. Radiomic features (SUV min , SUV correlation, CT min, CT busyness and CT coarseness) in 68 Ga-PSMA PET/ CT and clinical parameters such as Alp1 and Gleason score yielded strong correlations with changes in prostate-specific antigen (PSA) to predict outcome [58,59]. This finding also points in the direction of integrated diagnostics where the integration of multisource diagnostic data from medical imaging, pathology, liquid biopsy, and clinical findings is analyzed to achieve optimized diagnostic accuracy in evidence-based clinical decision guidance.…”

Section: Prostate Cancermentioning

confidence: 99%

Artificial Intelligence in Oncological Hybrid Imaging

Feuerecker

¹

,

Heimer

²

,

Geyer

³

et al. 2022

Rofo

View full text Add to dashboard Cite

Background Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes. Methods and Results The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations. Conclusion AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation. Key Points: Citation Format

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Analyzing the potential of radiomics features and radiomics signature from pretherapeutic PSMA-PET-CT scans and clinical data for prediction of overall survival when treated with Lu[177]-PSMA

Cited by 5 publications

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Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA

Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA

Artificial Intelligence in Oncological Hybrid Imaging

Artificial Intelligence in Oncological Hybrid Imaging

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