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Abstract Background The clinical utility of radiomics is hampered by a high correlation between the large number of features analysed which may result in the “bouncing beta” phenomenon which could in part explain why in a similar patient population texture features identified and/or cut-off values of prognostic significance differ from one study to another. Principal component analysis (PCA) is a technique for reducing the dimensionality of large datasets containing highly correlated variables, such as texture feature datasets derived from FDG PET images, increasing data interpretability whilst at the same time minimizing information loss by creating new uncorrelated variables that successively maximize variance. Here, we report on PCA of a texture feature dataset derived from 123 malignant melanoma lesions with a significant range in lesion size using the freely available LIFEx software. Results Thirty-eight features were derived from all lesions. All features were standardized. The statistical assumptions for carrying out PCA analysis were met. Seven principal components with an eigenvalue > 1 were identified. Based on the “elbow sign” of the Scree plot, only the first five were retained. The contribution to the total variance of these components derived using Varimax rotation was, respectively, 30.6%, 23.6%, 16.1%, 7.4% and 4.1%. The components provided summarized information on the locoregional FDG distribution with an emphasis on high FDG uptake regions, contrast in FDG uptake values (steepness), tumour volume, locoregional FDG distribution with an emphasis on low FDG uptake regions and on the rapidity of changes in SUV intensity between different regions. Conclusions PCA allowed to reduce the dataset of 38 features to a set of 5 uncorrelated new variables explaining approximately 82% of the total variance contained within the dataset. These principal components may prove more useful for multiple regression analysis considering the relatively low numbers of patients usually included in clinical trials on FDG PET texture analysis. Studies assessing the superior differential diagnostic, predictive or prognostic value of principal components derived using PCA as opposed to the initial texture features in clinical relevant settings are warranted.

Simple Summary: Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma have potential to serve as an alternative for non-invasive Gleason score characterization, for the prediction of biochemical recurrence and to differentiate benign from malignant increased tracer uptake. However, prior to their implementation in clinical practice, additional, clinically relevant studies performed according to recently published guidelines and checklists, offering full transparency, including large enough datasets as well as external validation, are mandatory. 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. [ABSTRACT FROM AUTHOR]

Prostate-specific membrane antigen (PSMA), a transmembrane glycoprotein, was shown to be expressed 100–1000 fold higher in prostate adenocarcinoma as compared to normal prostate epithelium. Given the enzymatic function of PSMA with the presence of an internalization triggering motif, various Glu-urea-Lys-based inhibitors have been developed and, amongst others, radiolabeled with positron emitters for targeted positron emission tomography imaging such as 68Ga-PSMA-HBED-CC Glu-urea-Lys(Ahx) as well as with beta and alpha-emitting radioisotopes for targeted therapy, e.g., 177Lu-PSMA-617. In this paper, we review and discuss the potential implications for targeted imaging and therapy of altered PSMA-glycosylation, of PSMA-driven activation of the P13K/Akt/mTOR, of the evolution over time and the relationship with androgen signaling and changes in DNA methylation of PSMA, and of androgen deprivation therapy (ADT) in prostate carcinoma. [ABSTRACT FROM AUTHOR]

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone. [ABSTRACT FROM AUTHOR]

Simple Summary: Hypoxia is a phenomenon common in cervical cancer. Both the presence and function of CAFs are upregulated in a hypoxic environment. A key factor in the physiological response to hypoxia is hypoxia-inducible factor-1alpha (HIF-1 α). We hypothesized that [68Ga]Ga-FAPI PET may be used as an indirect tracer for mapping hypoxia by correlating the image findings to pathological analysis of HIF-1 α expression. The maximum and mean standardized uptake value (SUVmax and SUVmean) and FAPI tumor volume (FAPI-TV) were documented. There was uptake of tracer in the pelvis (cervix region) in all patients studied. All patients had lymph node metastases, while only six patients had distant visceral or skeletal metastases. The average FAPI-TV for patients with additional sites of metastases was higher than those without. Immunohistochemistry revealed varying intensities of HIF-1 α expression in all tested samples. The presence of skeletal metastasis was correlated to the HIF-1⍺ staining (percentage distribution). Furthermore, the FAPI-TV was a better predictor of metastatic disease than the SUVmax. Hypoxia leads to changes in tumor microenvironment (upregulated CAFs) with resultant aggressiveness. A key factor in the physiological response to hypoxia is hypoxia-inducible factor-1alpha (HIF-1 α). [68Ga]Ga-FAPI PET imaging has been demonstrated in various cancer types. We hypothesized that [68Ga]Ga-FAPI PET may be used as an indirect tracer for mapping hypoxia by correlating the image findings to pathological analysis of HIF-1 α expression. The [68Ga]Ga-FAPI PET/CT scans of women with cancer of the cervix were reviewed and the maximum and mean standardized uptake value (SUVmax and SUVmean) and FAPI tumor volume (FAPI-TV) were documented. Correlation analysis was performed between PET-derived parameters and immunohistochemical staining as well as between PET-derived parameters and the presence of metastasis. Ten women were included. All patients demonstrated tracer uptake in the primary site or region of the primary. All patients had lymph node metastases while only six patients had distant visceral or skeletal metastases. The mean SUVmax, SUVmean, and FAPI-TV was 18.89, 6.88, and 195.66 cm3, respectively. The average FAPI-TV for patients with additional sites of metastases was higher than those without. Immunohistochemistry revealed varying intensities of HIF-1 α expression in all tested samples. There was a positive correlation between the presence of skeletal metastases and staining for HIF-1 α   (r = 0.80 ; p = 0.017) . The presence of skeletal metastasis was correlated to the HIF-1⍺ staining (percentage distribution). Furthermore, the FAPI-TV was a better predictor of metastatic disease than the SUVmax. [ABSTRACT FROM AUTHOR]

Purpose: 225Ac-PSMA-617 has demonstrated good anti-tumor effect as a treatment option for metastatic castration-resistant prostate cancer (mCRPC) patients. No study has previously assessed treatment outcome and survival following 225Ac-PSMA-617 treatment of de novo metastatic hormone-sensitive prostate carcinoma (mHSPC) patients. Based on the potential side effects that are known and explained to the patients by the oncologist, some of the patients refused the standard treatment and are seeking alternative therapies. Thus, we report our preliminary findings in a retrospective series of 21 mHSPC patients that refused standard treatment options and were treated with 225Ac-PSMA-617. Methods: We retrospectively reviewed patients with histologically confirmed de novo treatment-naïve bone ± visceral mHSPC that were treated with 225Ac-PSMA-617 radioligand therapy (RLT). Inclusion criteria included an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, treatment-naive bone ± visceral mHSPC, and patients refusal for ADT ± docetaxel, abiraterone acetate, or enzalutamide. We evaluated the response to treatment using prostate-specific antigen (PSA) response and the progression-free survival (PFS) and overall survival (OS) as well as the toxicities. Results: Twenty-one mHSPC patients were included in this preliminary work. Following treatment, twenty patients (95%) had any decline in PSA and eighteen patients (86%) presented with a PSA decline of ≥ 50% including 4 patients in whom PSA became undetectable. A lower percentage decrease in PSA following treatment was associated with increased mortality and shorter progression-free survival. Overall, administration of 225Ac-PSMA-617 was well tolerated. The commonest toxicity seen was grade I/II dry mouth observed in 94% of patients. Conclusions: Given these favorable results, randomized prospective multicenter trials assessing the clinical value of 225Ac-PSMA-617 as a therapeutic agent for mHSPC administered either as monotherapy or administered concomitant with ADT are of interest. [ABSTRACT FROM AUTHOR]