Machine Learning Facilitates Hotspot Classification in PSMA-PET/CT with Nuclear Medicine Specialist Accuracy

The Homunculus of unspecific bone uptakes associated with PSMA-targeted tracers: a systematic review-based definition

PURPOSE

Prostate-Specific Membrane Antigen (PSMA)-targeted Positron Emission Tomography (PET) has revolutionised prostate cancer (PCa) diagnosis and treatment, offering superior diagnostic accuracy over traditional methods and enabling theragnostic applications. However, a significant diagnostic challenge has emerged with identifying unspecific bone uptakes (UBUs), which could lead to over-staging and inappropriate treatment decisions if misinterpreted. This systematic review explores the phenomenon of UBUs in PCa patients undergoing PSMA-PET imaging.

METHODS

Studies assessing the prevalence, topographical distribution, and potential clinical implications of UBUs were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

The percentage of PCa patients with UBUs on PSMA-PET scans ranged from 0 to 71.7%, depending on the radiopharmaceutical used, with [18F]PSMA-1007 showing the highest incidence. The ribs are the primary site of UBUs across all PSMA-targeted radiopharmaceuticals. The spine is the second most frequent UBU site for [68Ga]Ga-PSMA-11, [18F]DCFPyL, [18F]rhPSMA-7, while the pelvic girdle represents the second most frequent site for [18F]PSMA-1007. The average maximum Standardized Uptake Value (SUVmax) of UBUs varied from 3.4 to 7.7 and was generally lower than that of bone metastases.

CONCLUSIONS

Our findings underscore the need for heightened awareness and precise interpretation of UBUs to avoid potential over-staging and subsequent inappropriate treatment decisions. Considering the radiopharmaceutical used, PET-derived semiquantitative parameters, the topographical distribution of UBUs, and accurately evaluating the pre-test probability based on clinical and laboratory parameters may aid nuclear medicine physicians in interpreting PSMA-PET findings.

Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma

Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize the available literature data on radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving the way for a fully automated image reading in nuclear medicine. As for prediction to treatment outcome following 177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration in larger datasets of patients.

Biology-guided radiotherapy in metastatic prostate cancer: time to push the envelope?

The therapeutic landscape of metastatic prostate cancer has undergone a profound revolution in recent years. In addition to the introduction of novel molecules in the clinics, the field has witnessed a tremendous development of functional imaging modalities adding new biological insights which can ultimately inform tailored treatment strategies, including local therapies. The evolution and rise of Stereotactic Body Radiotherapy (SBRT) have been particularly notable in patients with oligometastatic disease, where it has been demonstrated to be a safe and effective treatment strategy yielding favorable results in terms of disease control and improved oncological outcomes. The possibility of debulking all sites of disease, matched with the ambition of potentially extending this treatment paradigm to polymetastatic patients in the not-too-distant future, makes Biology-guided Radiotherapy (BgRT) an attractive paradigm which can be used in conjunction with systemic therapy in the management of patients with metastatic prostate cancer.

[68Ga]PSMA-11 for positron emission tomography (PET) imaging of prostate-specific membrane antigen (PSMA)-positive lesions in men with prostate cancer

INTRODUCTION

Theranostics targeting prostate-specific membrane antigen (PSMA) represent a new targeted approach for prostate cancer care that combines diagnostic and therapeutic radiopharmaceuticals to diagnose and treat the disease. Positron emission tomography (PET) is the imaging method of choice and several diagnostic radiopharmaceuticals for quantifying PSMA have received FDA approval and are in clinical use. [68Ga]Ga-PSMA-11 is one such imaging agent and the focus of this article. One beta-emitting radioligand therapy ([177Lu]Lu-PSMA-617) has also received FDA approval for prostate cancer treatment, and several other alpha- and beta-emitting radioligand therapies are in clinical trials.

AREAS COVERED

Theranostics targeting PSMA in men with prostate cancer are discussed with a focus on use of [68Ga]Ga-PSMA-11 for imaging PSMA-positive lesions in men with prostate cancer. The review covers [68Ga]Ga-PSMA-11 manufacture, current regulatory status, comparison of [68Ga]Ga-PSMA-11 to other imaging techniques, clinical updates, and emerging applications of artificial intelligence for [68Ga]Ga-PSMA-11 PET.

EXPERT OPINION

[68Ga]Ga-PSMA-11 is used in conjunction with a PET/CT scan to image PSMA positive lesions in men with prostate cancer. It is manufactured by chelating precursor with68Ga, either from a generator or cyclotron, and has regulatory approval around the world. It is widely used clinically in conjunction with radioligand therapies like [177Lu]Lu-PSMA-617.

A convolutional neural network-based system for fully automatic segmentation of whole-body [68Ga]Ga-PSMA PET images in prostate cancer

PURPOSE

The aim of this study was development and evaluation of a fully automated tool for the detection and segmentation of mPCa lesions in whole-body [68Ga]Ga-PSMA-11 PET scans by using a nnU-Net framework.

METHODS

In this multicenter study, a cohort of 412 patients from three different center with all indication of PCa who underwent [68Ga]Ga-PSMA-11 PET/CT were enrolled. Two hundred cases of center 1 dataset were used for training the model. A fully 3D convolutional neural network (CNN) is proposed which is based on the self-configuring nnU-Net framework. A subset of center 1 dataset and cases of center 2 and center 3 were used for testing of model. The performance of the segmentation pipeline that was developed was evaluated by comparing the fully automatic segmentation mask with the manual segmentation of the corresponding internal and external test sets in three levels including patient-level scan classification, lesion-level detection, and voxel-level segmentation. In addition, for comparison of PET-derived quantitative biomarkers between automated and manual segmentation, whole-body PSMA tumor volume (PSMA-TV) and total lesions PSMA uptake (TL-PSMA) were calculated.

RESULTS

In terms of patient-level classification, the model achieved an accuracy of 83%, sensitivity of 92%, PPV of 77%, and NPV of 91% for the internal testing set. For lesion-level detection, the model achieved an accuracy of 87-94%, sensitivity of 88-95%, PPV of 98-100%, and F1-score of 93-97% for all testing sets. For voxel-level segmentation, the automated method achieved average values of 65-70% for DSC, 72-79% for PPV, 53-58% for IoU, and 62-73% for sensitivity in all testing sets. In the evaluation of volumetric parameters, there was a strong correlation between the manual and automated measurements of PSMA-TV and TL-PSMA for all centers.

CONCLUSIONS

The deep learning networks presented here offer promising solutions for automatically segmenting malignant lesions in prostate cancer patients using [68Ga]Ga-PSMA PET. These networks achieve a high level of accuracy in whole-body segmentation, as measured by the DSC and PPV at the voxel level. The resulting segmentations can be used for extraction of PET-derived quantitative biomarkers and utilized for treatment response assessment and radiomic studies.

A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging

Prostate cancer is the second most common cause of malignancy among men, with bone metastasis being a significant source of morbidity and mortality in advanced cases. Detecting and treating bone metastasis at an early stage is crucial to improve the quality of life and survival of prostate cancer patients. This objective strongly relies on imaging studies. While CT and MRI have their specific utilities, they also possess certain drawbacks. Bone scintigraphy, although cost-effective and widely available, presents high false-positive rates. The emergence of PET/CT and PET/MRI, with their ability to overcome the limitations of standard imaging methods, offers promising alternatives for the detection of bone metastasis. Various radiotracers targeting cell division activity or cancer-specific membrane proteins, as well as bone seeking agents, have been developed and tested. The use of positron-emitting isotopes such as fluorine-18 and gallium-68 for labeling allows for a reduced radiation dose and unaffected biological properties. Furthermore, the integration of artificial intelligence (AI) and radiomics techniques in medical imaging has shown significant advancements in reducing interobserver variability, improving accuracy, and saving time. This article provides an overview of the advantages and limitations of bone scan using SPECT and SPECT/CT and PET imaging methods with different radiopharmaceuticals and highlights recent developments in hybrid scanners, AI, and radiomics for the identification of prostate cancer bone metastasis using molecular imaging.

Review of artificial intelligence clinical applications in Nuclear Medicine

This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer's disease and Parkinson's disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.

Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [18F]F-FDG Tracers - Part I. PSMA, Choline, and DOTA Radiotracers

Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [18F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [18F]F-FDG. The novel non-[18F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions' location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[18F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [18F]F-FDG.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training (n = 45) and testing (n = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTVprostate) on the pCT images; feature extraction from the CTVprostate using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation. The radiomics model and signature performances were evaluated based on the area under the receiver operating characteristic curve (AUC) as well as accuracy, sensitivity and specificity. This study's results show that our pCT-based radiomics model was able to predict the six-year progression-free survival of the high-risk localized PCa patients who received the WPRT with highly consistent performances (mean AUC: 0.76 (training) and 0.71 (testing)). These are comparable to findings of other similar studies including those using magnetic resonance imaging (MRI)-based radiomics. The accuracy, sensitivity and specificity of our radiomics signature that consisted of two texture features were 0.778, 0.833 and 0.556 (training) and 0.842, 0.867 and 0.750 (testing), respectively. Since CT is more readily available than MRI and is the standard-of-care modality for PCa WPRT planning, pCT-based radiomics could be used as a routine non-invasive approach to the prognostic prediction of WPRT treatment outcomes in high-risk localized PCa.

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review

Background

Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts.

Methods

Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias.

Results

More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment.

Discussion

Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study

Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Haralick texture features extracted from Ga-68 PSMA PET/CT to differentiate normal prostate from prostate cancer: a feasibility study

OBJECTIVE

Role of texture parameters on the basis of Ga-68 PSMA PET/CT in prostate cancer (Pca) is largely unexplored. Present work done is a preliminary study that aims to evaluate the role of Haralick texture features on the basis of Ga-68 PSMA PET/CT in Pca in which texture features were used to differentiate between normal prostate and Pca tissue.

METHODS

The study retrospectively enrolled patients in two groups: group 1 included 30 patients with biopsy-proven adenocarcinoma prostate and median age 64 years (range: 50-82 years) who underwent baseline Ga-68 PSMA PET/CT prior to therapy; group 2 included 24 patients with pathologies other than Pca and median age 53.5 years (range: 18-80 years) who underwent Ga-68 PSMA PET/CT as part of another study in our department. Patients in group 2 did not have any prostate pathology and served as controls for the study. The segmented images of prostate (3-D image) were used to calculate 11 Haralick texture features in MATLAB. SUVmax was also evaluated. All parameters were compared among the two groups using appropriate statistical analysis and P value <0.05 was considered significant.

RESULTS

All 11 Haralick texture features, as well as SUVmax, were significantly different among Pca and controls (P < 0.05). Among the texture features, contrast was most significant (P value of Mann-Whitney U <0.001) in differentiating Pca from normal prostate with AUROC curve of 82.9% with sensitivity and specificity 83.30% and 73.30%, respectively at cut-off 0.640. SUVmax was also significant with AUROC curve 94.0% and sensitivity and specificity 62.5% and 90%, respectively at cut-off 5.7. A significant negative correlation of SUVmax was observed with contrast.

CONCLUSION

Haralick texture features have a significant role in differentiating Pca and normal prostate.

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of the disease difficult. The evolution of the disease leads to spatial heterogeneity that is extremely difficult to characterise with solid biopsies. Imaging provides the opportunity to quantify disease spread. Advanced image analytics methods, including radiomics, offer the opportunity to characterise heterogeneity beyond what can be achieved with simple assessment. Radiomics analysis has the potential to yield useful quantitative imaging biomarkers that can improve the early detection of mPCa, predict disease progression, assess response, and potentially inform the choice of treatment procedures. Traditional radiomics analysis involves modelling with hand-crafted features designed using significant domain knowledge. On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention. Radiomics models have the potential to become vital pieces in the oncology workflow, however, the current limitations of the field, such as limited reproducibility, are impeding their translation into clinical practice. This review provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow. The current landscape of publications utilising radiomics methods in the assessment and treatment of mPCa are surveyed and reviewed. Associated studies have incorporated information from multiple imaging modalities, including bone scintigraphy, CT, PET with varying tracers, multiparametric MRI together with clinical covariates, spanning the prediction of progression through to overall survival in varying cohorts. The methodological quality of each study is quantified using the radiomics quality score. Multiple deficits were identified, with the lack of prospective design and external validation highlighted as major impediments to clinical translation. These results inform some recommendations for future directions of the field.

Molecular Imaging in Primary Staging of Prostate Cancer Patients: Current Aspects and Future Trends

Accurate primary staging is the cornerstone in all malignancies. Different morphological imaging modalities are employed in the evaluation of prostate cancer (PCa). Regardless of all developments in imaging, invasive histopathologic evaluation is still the standard method for the detection and staging of the primary PCa. Magnetic resonance imaging (MRI) and computed tomography (CT) play crucial roles; however, functional imaging provides additional valuable information, and it is gaining ever-growing acceptance in the management of PCa. Targeted imaging with different radiotracers has remarkably evolved in the past two decades. [111In]In-capromab pendetide scintigraphy was a new approach in the management of PCa. Afterwards, positron emission tomography (PET) tracers such as [11C/18F]choline and [11C]acetate were developed. Nevertheless, none found a role in the primary staging. By introduction of the highly sensitive small molecule prostate-specific membrane antigen (PSMA) PET/CT, as well as recent developments in MRI and hybrid PET/MRI systems, non-invasive staging of PCa is being contemplated. Several studies investigated the role of these sophisticated modalities in the primary staging of PCa, showing promising results. Here, we recapitulate the role of targeted functional imaging. We briefly mention the most popular radiotracers, their diagnostic accuracy in the primary staging of PCa, and impact on patient management.

Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans

The importance of machine learning (ML) in the clinical environment increases constantly. Differentiation of pathological from physiological tracer-uptake in positron emission tomography/computed tomography (PET/CT) images is considered time-consuming and attention intensive, hence crucial for diagnosis and treatment planning. This study aimed at comparing and validating supervised ML algorithms to classify pathological uptake in prostate cancer (PC) patients based on prostate-specific membrane antigen (PSMA)-PET/CT. Retrospective analysis of ⁶⁸Ga-PSMA-PET/CTs of 72 PC patients resulted in a total of 77 radiomics features from 2452 manually delineated hotspots for training and labeled pathological (1629) or physiological (823) as ground truth (GT). As the held-out test dataset, 331 hotspots (path.:128, phys.: 203) were delineated in 15 other patients. Three ML classifiers were trained and ranked to assess classification performance. As a result, a high overall average performance (area under the curve (AUC) of 0.98) was achieved, especially to detect pathological uptake (0.97 mean sensitivity). However, there is still room for improvement to detect physiological uptake (0.82 mean specificity), especially for glands. The ML algorithm applied to manually delineated lesions predicts hotspot labels with high accuracy on unseen data and may be an important tool to assist in clinical diagnosis.

Applications of artificial intelligence in nuclear medicine image generation

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed.

Decision-support for treatment with 177Lu-PSMA: machine learning predicts response with high accuracy based on PSMA-PET/CT and clinical parameters

Background

Treatment with radiolabeled ligands to prostate-specific membrane antigen (PSMA) is gaining importance in the treatment of patients with advanced prostate carcinoma. Previous imaging with positron emission tomography/computed tomography (PET/CT) is mandatory. The aim of this study was to investigate the role of radiomics features in PSMA-PET/CT scans and clinical parameters to predict response to ¹⁷⁷Lu-PSMA treatment given just baseline PSMA scans using state-of-the-art machine learning (ML) methods.

Methods

A total of 2,070 pathological hotspots annotated in 83 prostate cancer patients undergoing PSMA therapy were analyzed. Two main tasks are performed: (I) analyzing correlation of averaged (per patient) values of radiomics features of individual hotspots and clinical parameters with difference in prostate specific antigen levels (ΔPSA) in pre- and post-therapy as a therapy response indicator. (II) ML-based classification of patients into responders and non-responders based on averaged features values and clinical parameters. To achieve this, machine learning (ML) algorithms and linear regression tests are applied. Grid search, cross validation (CV) and permutation test were performed to assure that the results were significant.

Results

Radiomics features (PET_Min, PET_Correlation, CT_Min, CT_Busyness and CT_Coarseness) and clinical parameters such as Alp1 and Gleason score showed best correlations with ΔPSA. For the treatment response prediction task, 80% area under the curve (AUC), 75% sensitivity (SE), and 75% specificity (SP) were obtained, applying ML support vector machine (SVM) classifier with radial basis function (RBF) kernel on a selection of radiomics features and clinical parameters with strong correlations with ΔPSA.

Conclusions

Machine learning based on ⁶⁸Ga-PSMA PET/CT radiomics features holds promise for the prediction of response to ¹⁷⁷Lu-PSMA treatment, given only base-line ⁶⁸Ga-PSMA scan. In addition, it was shown that, the best correlating set of radiomics features with ΔPSA are superior to clinical parameters for this therapy response prediction task using ML classifiers.

Effect of Point-Spread Function Reconstruction for Indeterminate PSMA-RADS-3A Lesions on PSMA-Targeted PET Imaging of Men with Prostate Cancer

Purpose: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) is emerging as an important modality for imaging patients with prostate cancer (PCa). As with any imaging modality, indeterminate findings will arise. The PSMA reporting and data system (PSMA-RADS) version 1.0 codifies indeterminate soft tissue findings with the PSMA-RADS-3A moniker. We investigated the role of point-spread function (PSF) reconstructions on categorization of PSMA-RADS-3A lesions. Methods: This was a post hoc analysis of an institutional review board approved prospective trial. Around 60 min after the administration of 333 MBq (9 mCi) of PSMA-targeted ¹⁸F-DCFPyL, patients underwent PET/computed tomography (CT) acquisitions from the mid-thighs to the skull vertex. The PET data were reconstructed with and without PSF. Scans were categorized according to PSMA-RADS version 1.0, and all PSMA-RADS-3A lesions on non-PSF images were re-evaluated to determine if any could be re-categorized as PSMA-RADS-4. The maximum standardized uptake values (SUVs) of the lesions, mean SUVs of blood pool, and the ratios of those values were determined. Results: A total of 171 PSMA-RADS-3A lesions were identified in 30 patients for whom both PSF reconstructions and cross-sectional imaging follow-up were available. A total of 13/171 (7.6%) were re-categorized as PSMA-RADS-4 lesions with PSF reconstructions. A total of 112/171 (65.5%) were found on follow-up to be true positive for PCa, with all 13 of the re-categorized lesions being true positive on follow-up. The lesions that were re-categorized trended towards having higher SUV_max-lesion and SUV_max-lesion/SUV_mean-blood-pool metrics, although these relationships were not statistically significant. Conclusions: The use of PSF reconstructions for ¹⁸F-DCFPyL PET can allow the appropriate re-categorization of a small number of indeterminate PSMA-RADS-3A soft tissue lesions as more definitive PSMA-RADS-4 lesions. The routine use of PSF reconstructions for PSMA-targeted PET may be of value at those sites that utilize this technology.

Estimating 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 177Lu-PSMA

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PSMA-PET/CT) scans can facilitate diagnosis and treatment of prostate disease. Radiomics signature (RS) is widely used for the analysis of overall survival (OS) in cancer diseases. This study aims at investigating the role of radiomics features (RFs) and RS from pretherapeutic gallium-68 (⁶⁸Ga)-PSMA-PET/CT findings and patient-specific clinical parameters to analyze overall survival of prostate cancer (PC) patients when treated with lutethium-177 (¹⁷⁷Lu)-PSMA. A cohort of 83 patients with advanced PC was retrospectively analyzed. Average values of 73 RFs of 2070 malignant hotspots as well as 22 clinical parameters were analyzed for each patient. From the Cox proportional hazard model, the least absolute shrinkage and selection operator (LASSO) regularization method is used to select most relevant features (standardized uptake value (SUV)_Min and kurtosis with the coefficients of 0.984 and −0.118, respectively) and to calculate the RS from the RFs. Kaplan–Meier (KM) estimator was used to analyze the potential of RFs and conventional clinical parameters, such as metabolic tumor volume (MTV) and standardized uptake value (SUV) for the prediction of survival. As a result, SUV_Min, kurtosis, the calculated RS, SUV_Mean, as well as Hemoglobin (Hb)1, C-reactive protein (CRP)1, and ECOG1 (clinical parameters) achieved p-values less than 0.05, which suggest the potential of findings from ⁶⁸Ga-PSMA-PET/CT scans as well as patient-specific clinical parameters for the prediction of OS for patients with advanced PC treated with ¹⁷⁷Lu-PSMA therapy.