The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer

Combined clinical variable and radiomics of post-treatment total body scan for prediction of successful I-131 ablation in low-risk papillary thyroid carcinoma patients

To explore the feasibility of combined radiomics of post-treatment I-131 total body scan (TBS) and clinical parameter to predict successful ablation in low-risk papillary thyroid carcinoma (PTC) patients. Data of low-risk PTC patients who underwent total/near total thyroidectomy and I-131 ablation 30 mCi between April 2015 and July 2021 were retrospectively reviewed. The clinical factors studied included age, sex, and pre-ablative serum thyroglobulin (Tg). Radiomic features were extracted via PyRadiomics, and radiomic feature selection was performed. The predictive performance for successful ablation of the clinical parameter, radiomic, and combined models (radiomics combined with clinical parameter) was calculated using the area under the receiver operating characteristic curve (AUC). One hundred and thirty patients were included. Successful ablation was achieved in 77 patients (59.2%). The mean pre-ablative Tg in the unsuccessful group (15.50 ± 18.04 ng/ml) was statistically significantly higher than those in the successful ablation group (7.12 ± 7.15 ng/ml). The clinical parameter, radiomic, and combined models produced AUCs of 0.66, 0.77, and 0.87 in the training sets, and 0.65, 0.69, and 0.78 in the validation sets, respectively. The combined model produced a significantly higher AUC than that of the clinical parameter (p < 0.05). Radiomic analysis of the post-treatment TBS combined with pre-ablative serum Tg showed a significant improvement in the predictive performance of successful ablation in low-risk PTC patients compared to the use of clinical parameter alone.Thai Clinical Trials Registry TCTR identification number is TCTR20230816004 ( https://www.thaiclinicaltrials.org/show/TCTR20230816004 ).

Analytical performance validation of aPROMISE platform for prostate tumor burden, index and dominant tumor assessment with 18F-DCFPyL PET/CT. A pilot study

To validate the performance of automated Prostate Cancer Molecular Imaging Standardized Evaluation (aPROMISE) in quantifying total prostate disease burden with 18F-DCFPyL PET/CT and to evaluate the interobserver and histopathologic concordance in the establishment of dominant and index tumor. Patients with a recent diagnosis of intermediate/high-risk prostate cancer underwent 18F-DCFPyL-PET/CT for staging purpose. In positive-18F-DCFPyL-PET/CT scans, automated prostate tumor segmentation was performed using aPROMISE software and compared to an in-house semiautomatic-manual guided segmentation procedure. SUV and volume related variables were obtained with two softwares. A blinded evaluation of dominant tumor (DT) and index tumor (IT) location was assessed by both groups of observers. In histopathological analysis, Gleason, International Society of Urological Pathology (ISUP) group, DT and IT location were obtained. We compared all the obtained variables by both software packages using intraclass correlation coefficient (ICC) and Cohen's kappa coefficient (k) for the concordance analysis. Fifty-four patients with a positive 18F-DCFPyL PET/CT were evaluated. The ICC for the SUVmax, SUVpeak, SUVmean, tumor volume (TV) and total lesion activity (TLA) was: 1, 0.833, 0.615, 0.494 and 0.950, respectively (p < 0.001 in all cases). For DT and IT detection, a high agreement was observed between both softwares (k = 0.733; p < 0.001 and k = 0.812; p < 0.001, respectively) although the concordances with histopathology were moderate (p < 0001). The analytical validation of aPROMISE showed a good performance for the SUVmax, TLA, DT and IT definition in comparison to our in-house method, although the concordance was moderate with histopathology for DT and IT.

A Clinical Role of PET-MRI in Prostate Cancer?

PET/MRI is a relevant application field for prostate cancer management, offering advantages in early diagnosis, staging, and therapy planning. Despite drawbacks such as higher costs, longer acquisition time, and the need for skilled personnel, the technical integration of PET and MRI provides valuable information for detecting primary tumors, identifying metastases, and characterizing the disease, leading to more accurate staging and personalized treatment strategies. However, PET/MRI adoption has been slow, but ongoing technological advancements and AI integration might overcome challenges and improve clinical utility. As precision medicine gains importance in oncology, PET/MRI's multiparametric data can tailor treatment plans to individual patients, providing a comprehensive assessment of tumor biology and aggressiveness for more effective therapeutic strategies.

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.

CT-based deep learning model: a novel approach to the preoperative staging in patients with peritoneal metastasis

Peritoneal metastasis (PM) is a frequent manifestation of advanced abdominal malignancies. Accurately assessing the extent of PM before surgery is essential for patients to receive optimal treatment. Therefore, we propose to construct a deep learning (DL) model based on enhanced computed tomography (CT) images to stage PM preoperatively in patients. All 168 patients with PM underwent contrast-enhanced abdominal CT before either open surgery or laparoscopic exploration, and peritoneal cancer index (PCI) was used to evaluate patients during the surgical procedure. DL features were extracted from portal venous-phase abdominal CT scans and subjected to feature selection using the Spearman correlation coefficient and LASSO. The performance of models for preoperative staging was assessed in the validation cohort and compared against models based on clinical and radiomics (Rad) signature. The DenseNet121-SVM model demonstrated strong patient discrimination in both the training and validation cohorts, achieving AUC was 0.996 in training and 0.951 validation cohort, which were both higher than those of the Clinic model and Rad model. Decision curve analysis (DCA) showed that patients could potentially benefit more from treatment using the DL-SVM model, and calibration curves demonstrated good agreement with actual outcomes. The DL model based on portal venous-phase abdominal CT accurately predicts the extent of PM in patients before surgery, which can help maximize the benefits of treatment and optimize the patient's treatment plan.

Research on texture images and radiomics in urology: a review of urological MR imaging applications

PURPOSE OF REVIEW

Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives.

RECENT FINDINGS

Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization.

SUMMARY

Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.