AI and machine learning in medical imaging: key points from development to translation.

Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data.

Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.

Artificial intelligence in medicine: mitigating risks and maximizing benefits via quality assurance, quality control, and acceptance testing.

The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI.

In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.

Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer.

OBJECTIVES: The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. METHODS: This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. RESULTS: MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73-98%) and NPV of 93% (95%CI 82-98%), and for csPCa identification, with sensitivity of 91% (95%CI 72-99%) and NPV of 95% (95%CI 84-99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. CONCLUSION: MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. CLINICAL RELEVANCE STATEMENT: The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. KEY POINTS: • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice.

OBJECTIVES: To present the results of a survey on the assessment of treatment response with imaging in oncologic patient, in routine clinical practice. The survey was promoted by the European Society of Oncologic Imaging to gather information for the development of reporting models and recommendations. METHODS: The survey was launched on the European Society of Oncologic Imaging website and was available for 3 weeks. It consisted of 5 sections, including 24 questions related to the following topics: demographic and professional information, methods for lesion measurement, how to deal with diminutive lesions, how to report baseline and follow-up examinations, which previous studies should be used for comparison, and role of RECIST 1.1 criteria in the daily clinical practice. RESULTS: A total of 286 responses were received. Most responders followed the RECIST 1.1 recommendations for the measurement of target lesions and lymph nodes and for the assessment of tumor response. To assess response, 48.6% used previous and/or best response study in addition to baseline, 25.2% included the evaluation of all main time points, and 35% used as the reference only the previous study. A considerable number of responders used RECIST 1.1 criteria in daily clinical practice (41.6%) or thought that they should be always applied (60.8%). CONCLUSION: Since standardized criteria are mainly a prerogative of clinical trials, in daily routine, reporting strategies are left to radiologists and oncologists, which may issue local and diversified recommendations. The survey emphasizes the need for more generally applicable rules for response assessment in clinical practice. CRITICAL RELEVANCE STATEMENT: Compared to clinical trials which use specific criteria to evaluate response to oncological treatments, the free narrative report usually adopted in daily clinical practice may lack clarity and useful information, and therefore, more structured approaches are needed. KEY POINTS: · Most radiologists consider standardized reporting strategies essential for an objective assessment of tumor response in clinical practice. · Radiologists increasingly rely on RECIST 1.1 in their daily clinical practice. · Treatment response evaluation should require a complete analysis of all imaging time points and not only of the last.

Preoperative prostate biopsy and multiparametric magnetic resonance imaging: reliability in detecting prostate cancer

Purpose The aim of the study was to analyse and compare the ability of multiparametric magnetic resonance imaging (mp–MRI) and prostate biopsy (PB) to correctly identify tumor foci in patients undergoing radical prostatectomy (RP) for prostate cancer (PCa). Materials and Methods 157 patients with clinically localised PCa with a PSA <10 ng/mL and a negative DRE diagnosed on the first (12 samples, Group A) or second (18 samples, Group B) PB were enrolled at our institution. All patients underwent mp-MRI with T2-weighted images, diffusion-weighted imaging, dynamic contrast enhanced-MRI prior to RP. A map of comparison describing each positive biopsy sample was created for each patient, with each tumor focus shown on the MRI and each lesion present on the definitive histological examination in order to compare tumor detection and location. The sensitivity of mp-MRI and PB for diagnosis was compared using Student’s t-test. The ability of the two exams to detect the prevalence of Gleason pattern 4 in the identified lesions was compared using a chi-square test. Results Overall sensitivity of PB and mp-MRI to identify tumor lesion was 59.4% and 78.9%, respectively (p<0.0001). PB missed 144/355 lesions, 59 of which (16.6%) were significant. mp-MRI missed 75/355 lesions, 12 of which (3.4%) were significant. No lesions with a GS≥8 were missed. Sensitivity of PB and mp-MRI to detect the prevalence of Gleason pattern 4 was 88.2% and 97.4%, respectively. Conclusions mp-MRI seems to identify more tumor lesions than PB and to provide more information concerning tumor characteristics. .