Integration of magnetic resonance imaging and deep learning for prostate cancer detection: a systematic review.

Prostate Cancer Foci Detected on Multiparametric Magnetic Resonance Imaging are Histologically Distinct From Those Not Detected

The rapid advancement of artificial intelligence (AI) in medical imaging has generated significant interest and debate among healthcare professionals, researchers, and the general public. This study aims to explore trends and public perception of AI in medical imaging by analyzing social media discussions. Using a retrospective content analysis approach, social media posts from X (formerly known as Twitter) and Reddit were collected, covering discussions from 2019 to 2024. A total of 1,022 posts were analyzed after data cleaning, employing both qualitative and quantitative methods to examine sentiment, themes, and keyword frequencies. The sentiment analysis revealed that 55% of the comments expressed positive sentiments towards AI in medical imaging, emphasizing its potential to enhance diagnostic accuracy and efficiency. Neutral sentiments accounted for 35% of the posts, while 10% expressed negative sentiments, primarily focusing on concerns related to job displacement, ethical issues, and data privacy. Thematic analysis identified four primary themes: ethical and privacy concerns, job displacement, trust and reliability, and workflow efficiency. Keyword frequency analysis highlighted significant discussions around AI, imaging, and radiology. The results underscore both the optimism and concerns associated with AI in medical imaging, emphasizing the need for ongoing dialogue among technology developers, healthcare providers, and the public. Addressing ethical and privacy concerns, and integrating AI responsibly into clinical workflows, is crucial for maximizing its benefits and minimizing potential risks. These findings provide valuable insights into public perceptions and inform strategies for the effective and ethical implementation of AI technologies in healthcare.

To evaluate the incremental value of using diffusion-weighted magnetic resonance (MR) imaging in addition to T2-weighted imaging for the detection of prostate cancer in the transition zone and the assessment of tumor aggressiveness. This retrospective HIPAA-compliant institutional review board-approved study included 156 consecutive patients (median age, 59.2 years) who underwent MR imaging before radical prostatectomy. Two readers who were blinded to patient data independently recorded their levels of suspicion on a five-point scale of the presence of transition zone tumors on the basis of T2-weighted imaging alone and then, 4 weeks later, diffusion-weighted imaging and T2-weighted imaging together. Apparent diffusion coefficients (ADCs) were measured in transition zone cancers and glandular and stromal benign prostatic hyperplasia. Areas under the receiver operating characteristic curves were used to evaluate detection accuracy, and generalized linear models were used to test ADC differences between benign and malignant prostate regions. Whole-mount step-section histopathologic examination was the reference standard. In overall tumor detection, addition of diffusion-weighted imaging to T2-weighted imaging improved the areas under the receiver operating characteristic curves for readers 1 and 2 from 0.60 and 0.60 to 0.75 and 0.71, respectively, at the patient level (P = .004 for reader 1 and P = .027 for reader 2) and from 0.64 and 0.63 to 0.73 and 0.68, respectively, at the sextant level (P = .001 for reader 1 and P = .100 for reader 2). Least squares mean ADCs (× 10(-3) mm(2)/sec) in glandular and stromal benign prostatic hyperplasia were 1.44 and 1.09, respectively. Mean ADCs were inversely associated with tumor Gleason scores (1.10, 0.98, 0.87, and 0.75 for Gleason scores of 3 + 3, 3 + 4, 4 + 3, and ≥ 4 + 4, respectively). Use of diffusion-weighted imaging in addition to T2-weighted imaging improved detection of prostate cancer in the transition zone, and tumor ADCs were inversely associated with tumor Gleason scores in the transition zone.

To evaluate the incremental value of using diffusion-weighted magnetic resonance (MR) imaging in addition to T2-weighted imaging for the detection of prostate cancer in the transition zone and the assessment of tumor aggressiveness. This retrospective HIPAA-compliant institutional review board-approved study included 156 consecutive patients (median age, 59.2 years) who underwent MR imaging before radical prostatectomy. Two readers who were blinded to patient data independently recorded their levels of suspicion on a five-point scale of the presence of transition zone tumors on the basis of T2-weighted imaging alone and then, 4 weeks later, diffusion-weighted imaging and T2-weighted imaging together. Apparent diffusion coefficients (ADCs) were measured in transition zone cancers and glandular and stromal benign prostatic hyperplasia. Areas under the receiver operating characteristic curves were used to evaluate detection accuracy, and generalized linear models were used to test ADC differences between benign and malignant prostate regions. Whole-mount step-section histopathologic examination was the reference standard. In overall tumor detection, addition of diffusion-weighted imaging to T2-weighted imaging improved the areas under the receiver operating characteristic curves for readers 1 and 2 from 0.60 and 0.60 to 0.75 and 0.71, respectively, at the patient level (P = .004 for reader 1 and P = .027 for reader 2) and from 0.64 and 0.63 to 0.73 and 0.68, respectively, at the sextant level (P = .001 for reader 1 and P = .100 for reader 2). Least squares mean ADCs (× 10(-3) mm(2)/sec) in glandular and stromal benign prostatic hyperplasia were 1.44 and 1.09, respectively. Mean ADCs were inversely associated with tumor Gleason scores (1.10, 0.98, 0.87, and 0.75 for Gleason scores of 3 + 3, 3 + 4, 4 + 3, and ≥ 4 + 4, respectively). Use of diffusion-weighted imaging in addition to T2-weighted imaging improved detection of prostate cancer in the transition zone, and tumor ADCs were inversely associated with tumor Gleason scores in the transition zone.

ABSTRACTA revolution in medical diagnosis and treatment is being driven by the use of artificial intelligence (AI) in medical imaging. The diagnostic efficacy and accuracy of medical imaging are greatly enhanced by AI technologies, especially deep learning, that performs image recognition, feature extraction, and pattern analysis. Furthermore, AI has demonstrated significant promise in assessing the effects of treatments and forecasting the course of diseases. It also provides doctors with more advanced tools for managing the conditions of their patients. AI is poised to play a more significant role in medical imaging, especially in real‐time image processing and multimodal fusion. By integrating multiple forms of image data, multimodal fusion technology provides more comprehensive disease information, whereas real‐time image analysis can assist surgeons in making more precise decisions. By tailoring treatment regimens to each patient's unique needs, AI enhances both the effectiveness of treatment and the patient experience. Overall, AI in medical imaging promises a bright future, significantly enhancing diagnostic precision and therapeutic efficacy, and ultimately delivering higher‐quality medical care to patients.

To compare diagnostic accuracy of T2-weighted magnetic resonance (MR) imaging with that of multiparametric (MP) MR imaging combining T2-weighted imaging with diffusion-weighted (DW) MR imaging, dynamic contrast material-enhanced (DCE) MR imaging, or both in the detection of locally recurrent prostate cancer (PCa) after radiation therapy (RT). This retrospective HIPAA-compliant study was approved by the institutional review board; informed consent was waived. Fifty-three men (median age, 70 years) suspected of having post-RT recurrence of PCa underwent MP MR imaging, including DW and DCE sequences, within 6 months after biopsy. Two readers independently evaluated the likelihood of PCa with a five-point scale for T2-weighted imaging alone, T2-weighted imaging with DW imaging, T2-weighted imaging with DCE imaging, and T2-weighted imaging with DW and DCE imaging, with at least a 4-week interval between evaluations. Areas under the receiver operating characteristic curve (AUC) were calculated. Interreader agreement was assessed, and quantitative parameters (apparent diffusion coefficient [ADC], volume transfer constant [K(trans)], and rate constant [k(ep)]) were assessed at sextant- and patient-based levels with generalized estimating equations and the Wilcoxon rank sum test, respectively. At biopsy, recurrence was present in 35 (66%) of 53 patients. In detection of recurrent PCa, T2-weighted imaging with DW imaging yielded higher AUCs (reader 1, 0.79-0.86; reader 2, 0.75-0.81) than T2-weighted imaging alone (reader 1, 0.63-0.67; reader 2, 0.46-0.49 [P ≤ .014 for all]). DCE sequences did not contribute significant incremental value to T2-weighted imaging with DW imaging (reader 1, P > .99; reader 2, P = .35). Interreader agreement was higher for combinations of MP MR imaging than for T2-weighted imaging alone (κ = 0.34-0.63 vs κ = 0.17-0.20). Medians of quantitative parameters differed significantly (P < .0001 to P = .0233) between benign tissue and PCa (ADC, 1.64 × 10(-3) mm(2)/sec vs 1.13 × 10(-3) mm(2)/sec; K(trans), 0.16 min(-1) vs 0.33 min(-1); k(ep), 0.36 min(-1) vs 0.62 min(-1)). MP MR imaging has greater accuracy in the detection of recurrent PCa after RT than T2-weighted imaging alone, with no additional benefit if DCE is added to T2-weighted imaging and DW imaging.

Prostate MRI: Who, when, and how? Report from a UK consensus meeting

Magnetic resonance (MR) imaging has been established as the best imaging modality for the detection, localization, and staging of prostate cancer on account of its high resolution of soft tissue and the possibilities of multiplanar and multiparameter scanning. To evaluate T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), MR spectroscopy (MRS), and the combination of these three MR techniques in the diagnosis of prostate cancer, as correlated to histopathologic findings. MR imaging, including T2WI, DWI, and MRS, was performed at 1.5T with a body coil combined with a spine coil in 42 cases. Diagnosis was confirmed by histopathology through systematic transrectal prostate biopsy. Apparent diffusion coefficient (ADC) maps were obtained with two b values (0 and 1000 s/mm(2)). The metabolic maps of 3D-MRS imaging were analyzed for the ratio of (Cho+Cre)/Cit in each sextant. All cases were evaluated by T2WI, DWI, and MRS, and then by the three methods combined. The statistical indicators and the receiver operating characteristic (ROC) curve analysis of each method were compared to the results of histopathology obtained by transrectal prostate biopsy. 15 of 42 cases were confirmed to be cancerous, and 27 of 42 cases were noncancerous. All the 252 sextants were confirmed by biopsies, including 201 benign sextants and 51 malignant sextants. The sensitivity and the specificity for the detection of prostate cancer were 88.2% and 67.2% for T2WI, as the cutoff was 3; 82.4% and 81.6% for DWI, as the cutoff was 4; 84.3% and 98.0% for MRS, as the cutoff was 5; and 96.1% and 96.5% for the combined T2WI+DWI+MRS, as the cutoff was 4. In the ROC analysis, the correlative areas under the ROC curves (Az) were 0.848+/-0.030, 0.860+/-0.033, and 0.961+/-0.016 for T2WI, DWI, and MRS, respectively, and 0.978+/-0.009 for the combination of T2WI+DWI+MRS. The accuracy of the detection of prostate cancer is increased through a combination of the three techniques. Moreover, MRS demonstrated higher accuracy compared with T2WI or DWI.

Neoadjuvant immunochemotherapy (nICT) has demonstrated significant potential in improving pathological response rates and survival outcomes for patients with locally advanced esophageal squamous cell carcinoma (ESCC). However, substantial interindividual variability in therapeutic outcomes highlights the urgent need for more precise predictive tools to guide clinical decision-making. Traditional biomarkers remain limited in both predictive performance and clinical feasibility. In recent years, the application of artificial intelligence (AI) in medical imaging has expanded rapidly. By incorporating voxel-level feature maps, the combination of radiomics and deep learning enables the extraction of rich textural, morphological, and microstructural features, while autonomously learning high-level abstract representations from clinical CT images, thereby revealing biological heterogeneity that is often imperceptible to conventional assessments. Leveraging these high-dimensional representations, AI models can provide more accurate predictions of nICT response. Future advancements in foundation models, multimodal integration, and dynamic temporal modeling are expected to further enhance the generalizability and clinical applicability of AI. AI-powered medical imaging is poised to support all stages of perioperative management in ESCC, playing a pivotal role in high-risk patient identification, dynamic monitoring of therapeutic response, and individualized treatment adjustment, thereby comprehensively advancing precision nICT.

Multiparametric 3T Prostate Magnetic Resonance Imaging to Detect Cancer: Histopathological Correlation Using Prostatectomy Specimens Processed in Customized Magnetic Resonance Imaging Based Molds

In recent years, prostate-specific antigen (PSA) screening has been widely performed. As a result, patients who need to undergo a complete physical examination for an elevated PSA level have been rapidly increasing. Magnetic resonance imaging (MRI) examination has previously been reported to be effective for the detection of prostate cancer. To evaluate the detectability of prostate cancer by performing MRI before biopsy, and to evaluate the relationship between detectability with MRI and cancer location, Gleason score (GS), and tumor size. MRI was performed at 1.5 Tesla in 122 consecutive patients before biopsy. The detectability of prostate cancer, including sensitivity and positive predictive value (PPV) of transrectal ultrasonography (TRUS), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) (b=2000 s/mm(2)), apparent diffusion coefficient (ADC) map, and biopsy, was calculated using whole-mount section histopathology as a gold standard. In addition, the relationship between the detectability on each MRI sequence and factors such as cancer location (peripheral zone vs. transition zone), GS 5-10, short-axis diameter (< or =4 mm, 5-9 mm, > or =10 mm), and long-axis diameter (< or =9 mm, 10-19 mm, > or =20 mm) were also evaluated. The sensitivities of TRUS, T2WI, DWI, ADC map, and biopsy were 26.9%, 41.2%, 56.7%, 57.7%, and 75.1%, respectively, and the PPVs of those modalities were 73.0%, 83.0%, 86.4%, 87.2%, and 91.5%, respectively. There was no correlation between the sensitivity of each MRI sequence and cancer location. The sensitivity of each MRI sequence increased as GS and short- and long-axis diameters of cancer lesions increased. MRI before a biopsy has a high detectability of prostate cancer, particularly with tumor size of more than 5 mm in short-axis diameter or 10 mm in long-axis diameter.

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001). Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

Evidence-Based Artificial Intelligence in Medical Imaging

The rapid integration of artificial intelligence (AI) in medical imaging has transformed the healthcare landscape, enabling precision therapy for a range of diseases. This article explores the key roles of AI in medical imaging, particularly focusing on three vital areas: segmentation, laser-guided procedures, and protective shielding. AI-driven segmentation tools offer unprecedented accuracy in identifying pathological regions, improving diagnostic efficiency, and aiding in personalized treatment plans. Laser-guided procedures, powered by AI algorithms, provide enhanced precision in targeting affected tissues, minimizing damage to healthy tissues, and promoting faster recovery times. Additionally, AI has made significant strides in optimizing protective shielding techniques, ensuring patient safety while minimizing radiation exposure during imaging and therapy. The article discusses the technological advances, clinical applications, challenges, and future directions of AI in these domains. Through this synthesis, the potential of AI to revolutionize medical imaging and contribute to more effective, safer, and personalized therapies becomes evident.

To explore simultaneous magnetic resonance imaging (MRI) for multiple hepatoma-bearing rats in a single session suppressing motion- and flow-related artifacts to conduct preclinical cancer research efficiently. Our institutional Animal Experimental Committee approved this study. We acquired PROPELLER (periodically rotated overlapping parallel lines with enhanced reconstruction) T2 - and diffusion-weighted images of the liver in one healthy and 11 N1-S1 hepatoma-bearing rats in three sessions using a 3-T clinical scanner and dedicated multiarray coil. We compared tumor volumes on MR images and those on specimens, evaluated apparent diffusion coefficients (ADC) of the tumor, and compared them to previously reported values. Each MRI session took 39-50 minutes from anesthesia induction to the end of scans for four rats (10-13 minutes per rat). PROPELLER provided artifact-reduced T2 - and diffusion-weighted images of the rat livers. Tumor volumes on MR images ranged from 0.04-1.81 cm(3) and were highly correlated with those on specimens. The ADC was 1.57 ± 0.37 × 10(-3) mm(2) /s (average ± SD), comparable to previously reported values. PROPELLER allowed simultaneous acquisition of artifact-reduced T2 - and diffusion-weighted images of multiple hepatoma-bearing rats. This technique can promote high-throughput preclinical MR research for liver cancer.