BACKGROUND: At present, biopsy is essential for the diagnosis of prostate cancer (PCa) before radical prostatectomy (RP). However, with the development of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and multiparametric magnetic resonance imaging (mpMRI), it might be feasible to avoid biopsy before RP. Herein, we aimed to explore the feasibility of avoiding biopsy before RP in patients highly suspected of having PCa after assessment of PSMA PET/CT and mpMRI. METHODS: Between December 2017 and April 2022, 56 patients with maximum standardized uptake value (SUVmax) of ≥4 and Prostate Imaging Reporting and Data System (PI-RADS) ≥4 lesions who received RP without preoperative biopsy were enrolled from two tertiary hospitals. The consistency between clinical and pathological diagnoses was evaluated. Preoperative characteristics were compared among patients with different pathological types, T stages, International Society of Urological Pathology (ISUP) grades, and European Association of Urology (EAU) risk groups. RESULTS: Fifty-five (98%) patients were confirmed with PCa by pathology, including 49 (89%) with clinically significant prostate cancer (csPCa, defined as ISUP grade ≥2 malignancy). One patient was diagnosed with high-grade prostatic intraepithelial neoplasia (HGPIN). CsPCa patients, compared with clinically insignificant prostate cancer (cisPCa) and HGPIN patients, were associated with a higher level of prostate-specific antigen (22.9 ng/mL vs . 10.0 ng/mL, P = 0.032), a lower median prostate volume (32.2 mL vs . 65.0 mL, P = 0.001), and a higher median SUVmax (13.3 vs . 5.6, P <0.001). CONCLUSIONS It might be feasible to avoid biopsy before RP for patients with a high probability of PCa based on PSMA PET/CT and mpMRI. However, the diagnostic efficacy of csPCa with PI-RADS ≥4 and SUVmax of ≥4 is inadequate for performing a procedure such as RP. Further prospective multicenter studies with larger sample sizes are necessary to confirm our perspectives and establish predictive models with PSMA PET/CT and mpMRI.
Humans ; Male ; Prostatectomy/methods* ; Prostatic Neoplasms/diagnosis* ; Middle Aged ; Aged ; Positron Emission Tomography Computed Tomography/methods* ; Biopsy ; Multiparametric Magnetic Resonance Imaging ; Prostate-Specific Antigen/metabolism*

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Molecular Probes ; Molecular Imaging/methods* ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Tomography, Emission-Computed, Single-Photon ; Ultrasonography ; Optical Imaging ; Biomarkers, Tumor ; Precision Medicine/methods*

OBJECTIVES: To evaluate the value of ⁶⁸Ga-DOTATATE and ¹⁸F-FDG PET/CT imaging in staging and treatment decision for gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN). METHODS: This retrospective analysis was conducted in 49 patients with GEP-NEN undergoing ¹⁸F-FDG and ⁶⁸Ga-DOTATATE PET/CT imaging at our hospital from August, 2020 to March, 2023, including 34 newly diagnosed patients and 15 patients with recurrence or metastasis after treatment. GEP-NEN were classified into G1, G2, and G3 neuroendocrine tumors (NET) and neuroendocrine carcinomas (NEC) based on pathological typing. The detection efficiency were classified into 4 patterns based on the number of positive tumor lesions detected by the two tracers: ⁶⁸Ga-DOTATATE>¹⁸F-FDG (A); ⁶⁸Ga-DOTATATE=¹⁸F-FDG (B); ⁶⁸Ga-DOTATATE<¹⁸F-FDG (C); and complementation (D). The value of dual-modality imaging in staging and treatment decision were evaluated by visual analysis. RESULTS: In the 49 patients with GEP-NEN, ⁶⁸Ga-DOTATATE PET/CT was superior to ¹⁸F-FDG PET/CT for detecting systemic tumor lesions (P<0.001) and more sensitive for detecting primary/recurrent lesions, lymph node metastasis, liver metastasis, and bone metastasis (P<0.05), while ¹⁸F-FDG PET/CT had higher detection rates for lung metastasis and peritoneal metastasis (P<0.05). In terms of the detection efficiency, Pattern A was found in 46.9% (23/49) patients, Pattern B in 38.8% (19/49), Pattern C in 12.2% (6/49), and Pattern D in 2.0% (1/49). The complementary value of ¹⁸F-FDG PET/CT to ⁶⁸Ga-DOTATATE PET/CT was 0% in G1 NET patients (0/13), 8.3% in G2 NET patients (2/24), 50% in G3 NET patients (3/6), and 33.3% in NEC patients (2/6). 12.2% (6/49) of the patients had their staging confirmed or changed due to additional lesions detected by ¹⁸F-FDG PET/CT imaging, resulting subsequently in establishment or adjustment of their treatment plans. CONCLUSIONS ⁶⁸Ga-DOTATATE PET/CT imaging should be the primary choice for GEP-NEN patients. Additional ¹⁸F-FDG PET/CT imaging can potentially improve precision of staging and treatment decision-making for G2, G3 and NEC patients but provides virtually no clinical benefits for G1 NET patients.
Humans ; Positron Emission Tomography Computed Tomography/methods* ; Neuroendocrine Tumors/therapy* ; Pancreatic Neoplasms/therapy* ; Retrospective Studies ; Organometallic Compounds ; Stomach Neoplasms/therapy* ; Neoplasm Staging ; Fluorodeoxyglucose F18 ; Intestinal Neoplasms/therapy* ; Female ; Male ; Middle Aged ; Aged ; Adult

OBJECTIVE: To explore the diagnostic value of ¹⁸F-FDG PET/CT in bone marrow infiltration (BMI) of newly diagnosed diffuse large B-cell lymphoma (DLBCL), compared with the results of bone marrow biopsy (BMB) and investigate whether the BMI diagnosed by ¹⁸F-FDG PET/CT and other factors have independent prognostic values. METHODS: Ninety-four newly diagnosed DLBCL patients who underwent PET/CT in Clinical Medical College of Shanghai General Hospital of Nanjing Medical University were included. BMB was performed within 2 weeks before or after PET/CT, and standardized treatment was performed after PET/CT. The manifestations of bone marrow (BM) FDG uptake were recorded. The diagnostic criteria of BMI were BMB positive or focal BM FDG uptake confirmed by imaging follow-up. The relationship between clinical features and BM FDG uptake and the values of PET/CT and BMB in the diagnosis of BMI was analyzed. The progression-free survival (PFS) was analyzed by Kaplan-Meier survival curves, log-rank test was used to compare PFS rate, and Cox regression model was used to analyze the independent risk factors affecting PFS. RESULTS: Among 94 DLBCL patients, 34 patients showed focal BM uptake (fPET), 7 patients showed super BM uptake (sBMU), 11 patients showed diffuse homogenous uptake higher than liver (dPET), and the other 42 patients had normal BM uptake (nPET) (lower than liver). BMB positive was found in all sBMU patients, in 20.6%(7/34) of fPET patients, and in 27.3% (3/11) of dPET patients. All nPET patients had negative BMB results. dPET patients were associated with lower hemoglobin level and leukocyte count compared with nPET group (P < 0.001, P =0.026). Compared with fPET patients, sBMU patients were more likely to have B symptoms and elevated lactate dehydrogenase (LDH). A total of 44 patients were diagnosed BMI, including 17 cases with BMB⁺. The sensitivity and specificity of BMB in the diagnosis of BMI was 38.6% (17/44) and 100% (50/50), respectively. Using fPET and sBMU as criteria of PET BMI, the diagnostic sensitivity and specificity of PET/CT was 93.2% (41/44) and 100% (50/50), respectively. Kaplan-Meier analysis showed that there was no significant difference in 2-year PFS rate between nPET and dPET patients (P >0.05), while sBMU patients had lower 2-year PFS rate compared with fPET patients (P < 0.001). Multivariate analysis showed that higher Ann Arbor stage (HR=9.010, P =0.04) and sBMU (HR=3.964, P =0.002) were independent risk factors affecting PFS. CONCLUSIONS Increased BM FDG uptake of DLBCL can be manifested as dPET, fPET and sBMU. fPET and sBMU can replace BMB to diagnose BMI. Although dPET cannot completely exclude the possibility of BMI, it does not affect the prognosis, so it can be diagnosed as PET BMI negative. sBMU is an independent prognostic risk factor.
Humans ; Positron Emission Tomography Computed Tomography/methods* ; Fluorodeoxyglucose F18 ; Prognosis ; Bone Marrow/pathology* ; Retrospective Studies ; China ; Positron-Emission Tomography/methods* ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Biopsy

BACKGROUND: In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value. METHODS: Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups. RESULTS: Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66). CONCLUSIONS The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Positron Emission Tomography Computed Tomography ; Lung Neoplasms/drug therapy* ; Fluorodeoxyglucose F18/therapeutic use* ; Nomograms ; Retrospective Studies ; B7-H1 Antigen/metabolism* ; Glucose/therapeutic use* ; Positron-Emission Tomography/methods*

OBJECTIVE: Diffuse large B-cell lymphoma (DLBCL) is often associated with bone marrow infiltration, and 2-deoxy-2-(18F) fluorodeoxyglucose positron emission tomography/computed tomography ( ¹⁸F-FDG PET/CT) has potential diagnostic significance for bone marrow infiltration in DLBCL. METHODS: A total of 102 patients diagnosed with DLBCL between September 2019 and August 2022 were included. Bone marrow biopsy and ¹⁸F-FDG PET/CT examinations were performed at the time of initial diagnosis. Kappa tests were used to evaluate the agreement of ¹⁸F-FDG PET/CT with the gold standard, and the imaging features of DLBCL bone marrow infiltration on PET/CT were described. RESULTS: The total detection rate of bone marrow infiltration was not significantly different between PET/CT and primary bone marrow biopsy ( P = 0.302) or between the two bone marrow biopsies ( P = 0.826). The sensitivity, specificity, and Youden index of PET/CT for the diagnosis of DLBCL bone marrow infiltration were 0.923 (95% CI, 0.759-0.979), 0.934 (95% CI, 0.855-0.972), and 0.857, respectively. CONCLUSION ¹⁸F-FDG PET/CT has a comparable efficiency in the diagnosis of DLBCL bone marrow infiltration. PET/CT-guided bone marrow biopsy can reduce the misdiagnosis of DLBCL bone marrow infiltration.
Humans ; Positron Emission Tomography Computed Tomography/methods* ; Fluorodeoxyglucose F18 ; Bone Marrow/pathology* ; Retrospective Studies ; Positron-Emission Tomography/methods* ; Lymphoma, Large B-Cell, Diffuse/pathology*

The American Urological Association (AUA), European Association of Urology (EUA) and International Urological Society (SIU) annual meetings were held in 2022. Studies on prostate cancer reported in the meetings mainly focus on the advances of diagnostic biomarkers (such as α-2, 3-1inked sialylation of terminal N-glycan on free PSA density, SelectMDx) and imaging techniques [such as multiparametric magnetic resonance imaging, prostate specific membrane antigen(PSMA)-PET/CT], the new method for prostate biopsy, the new treatments of prostate cancer including [¹⁷⁷Lu] Ludotadipep and DROP-IN PSMA probe, and the prognosis assessment of prostate cancer (such as AR-V7). This article provides an overview on the research hotspots of three international academic meetings.
Male ; Humans ; Urology ; Positron Emission Tomography Computed Tomography/methods* ; Prostatic Neoplasms/pathology* ; Multiparametric Magnetic Resonance Imaging/methods* ; Gallium Radioisotopes

Biology-guided radiotherapy (BgRT) is a novel technique of external beam radiotherapy, combining positron emission tomography-computed tomography (PET-CT) with a linear accelerator (LINAC). The key innovation is to utilize PET signals from tracers in tumor tissues for real-time tracking and guiding beamlets. Compared with a traditional LINAC system, a BgRT system is more complex in hardware design, software algorithm, system integration and clinical workflow. RefleXion Medical has developed the world's first BgRT system. Nevertheless, its actively advertised function, PET-guided radiotherapy, is still in the research and development phase. In this review study, we presented a number of issues related to BgRT, including its technical advantages and potential challenges.
Positron Emission Tomography Computed Tomography ; Radiotherapy Planning, Computer-Assisted/methods* ; Algorithms ; Particle Accelerators ; Biology ; Radiotherapy, Image-Guided/methods* ; Radiotherapy Dosage

Cross-modality reconstruction of medical images refers to predicting the image from one modality to another so as to achieve more accurate personalized medicine. Generative adversarial networks is the most commonly used deep learning technique in cross-modality reconstruction. It can generate realistic images by learning features from implicit distributions that follow the distributions of real data and then reconstruct the image of another modality rapidly. With the sharp increase in clinical demand for multi-modality medical image, this technology has been widely used in the task of cross modal reconstruction between different medical image modalities, such as magnetic resonance imaging, computed tomography and positron emission computed tomography. It can achieve accurate and efficient cross-modality image reconstruction in different parts of the body, such as the brain, heart, etc. In addition, although GAN has achieved some success in cross-modality reconstruction, its stability, generalization ability, and accuracy still need further research and improvement.
Brain/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Magnetic Resonance Imaging/methods* ; Positron-Emission Tomography ; Tomography, X-Ray Computed