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OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort. METHODS: Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015-2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow. RESULTS: The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging. CONCLUSIONS: Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status. CLINICAL RELEVANCE STATEMENT: The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment. KEY POINTS: ⢠Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances. ⢠The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic. â¢Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.
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OBJECTIVES: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). METHODS: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. RESULTS: Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. CONCLUSIONS: MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. KEY POINTS: ⢠A radiomic model was used to classify PCa aggressiveness. ⢠Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. ⢠The most predictive features belong to the texture (57%) and intensity (43%) domains.
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Neoplasias de la Próstata , Humanos , Imagen por Resonancia Magnética , Masculino , Estudios Prospectivos , Neoplasias de la Próstata/diagnóstico por imagen , Neoplasias de la Próstata/radioterapia , Estudios RetrospectivosRESUMEN
PURPOSE: Peptide receptor radionuclide therapy (PRRT) with 90Y-labelled and 177Lu-labelled peptides is an effective strategy for the treatment of metastatic/nonresectable neuroendocrine tumours (NETs). Dosimetry provides important information useful for optimizing PRRT with individualized regimens to reduce toxicity and increase tumour responses. However, this strategy is not applied in routine clinical practice, despite the fact that several dosimetric studies have demonstrated significant dose-effect correlations for normal organ toxicity and tumour response that can better guide therapy planning. The present study reviews the key relationships and the radiobiological models available in the literature with the aim of providing evidence that optimization of PRRT is feasible through the implementation of dosimetry. METHODS: The MEDLINE database was searched combining specific keywords. Original studies published in the English language reporting dose-effect outcomes in patients treated with PRRT were chosen. RESULTS: Nine of 126 studies were selected from PubMed, and a further five were added manually, reporting on 590 patients. The studies were analysed and are discussed in terms of weak and strong elements of correlations. CONCLUSION: Several studies provided evidence of clinical benefit from the implementation of dosimetry in PRRT, indicating the potential contribution of this approach to reducing severe toxicity and/or reducing undertreatment that commonly occurs. Prospective trials, possibly multicentre, with larger numbers of patients undergoing quantitative dosimetry and with standardized methodologies should be carried out to definitively provide robust predictive paradigms to establish effective tailored PRRT.
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Lutecio/efectos adversos , Lutecio/uso terapéutico , Medicina de Precisión/métodos , Radioisótopos/efectos adversos , Radioisótopos/uso terapéutico , Planificación de la Radioterapia Asistida por Computador/métodos , Receptores de Péptidos/metabolismo , Radioisótopos de Itrio/efectos adversos , Radioisótopos de Itrio/uso terapéutico , Humanos , Dosificación RadioterapéuticaRESUMEN
BACKGROUND: Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models. PURPOSE: To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations. MATERIALS AND METHODS: Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves. RESULTS: Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78. CONCLUSION: By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.
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OBJECTIVE: Magnetic resonance often produces feelings of anxiety before, or during, the examination. The aim of this study was to assess anxiety and potential causes of anxiety in cancer patients undergoing whole-body magnetic resonance imaging (WB-MRI). METHODS: This monocentric study recruited 70 cancer patients who were scheduled to undergo WB-MRI for detection, staging or therapy monitoring. At baseline (prior to the WB-MRI), assessments were performed using the State-Trait Anxiety Inventory (STAI-Y 1), Illness Perception Questionnaire (IPQ-R), Big Five Inventory (BIF-10) and Revised Life Orientation Test (LOT-R), while at the end of the WB-MRI examination the patients repeated the STAI-Y 1 questionnaire and were asked to indicate their preference between WB-MRI and computed tomography. RESULTS: We found a positive correlation between pre- and post-examination STAI-Y 1 scores (r = 0.536, p < .0001), with no significant difference between them. Pre-examination STAI-Y 1 scores had a negative correlation with the emotional stability in the BIF-10 questionnaire (r = -0.47, p = .001) and a positive correlation with emotional representation (r = 0.57, p = .001) in IPQ-R. The post-examination STAI-Y 1 had a negative correlation with optimistic orientation (r = -0.59, p = .001). CONCLUSIONS: The anxiety associated with a WB-MRI examination was only in small part associated with the examination itself, and in fact, most patients preferred WB-MRI to computed tomography. Concern with the outcome of the examination was likely a greater source of anxiety.
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Imagen por Resonancia Magnética , Neoplasias , Humanos , Imagen por Resonancia Magnética/métodos , Imagen de Cuerpo Entero/métodos , Ansiedad/diagnóstico , Ansiedad/etiología , Tomografía Computarizada por Rayos X/métodos , Neoplasias/complicacionesRESUMEN
We read with great interest the paper entitled "Impact of physical activity of cognitive functions: a new field for research and management of Cystic Fibrosis" by Elce et al. [...].
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OBJECTIVE: To evaluate the satisfaction of asymptomatic subjects who self-referring Whole-Body Magnetic Resonance Imaging (WB-MRI) for early cancer diagnosis. METHODS: Subjects completed a pre-examination questionnaire, while waiting for their WB-MRI examination, recording demographics, expected discomfort, perceived knowledge and usefulness of the procedure and health risk perceptions, as well as a post-examination questionnaire, measuring discomfort experienced, acceptability and satisfaction with WB-MRI. We examined which factors influenced discomfort and satisfaction associated with WB-MRI. RESULTS: 65 asymptomatic subjects (median age 51; 29 females) completed the questionnaire. Before WB-MRI, 29% of subjects expected discomfort of some form with claustrophobia (27.7%) and exam duration (24.6%) being the most common concerns. Experienced discomfort due to shortness of breath was significantly lower than expected. This difference was significantly associated with the personal risk perception to get a disease (p = 0.01) and educational level (p = 0.002). More specifically, higher level of perceived personal risk of getting a disease and lower level of education were associated with higher expected than experienced discomfort. Similarly, experiencing less claustrophobia than expected was significantly associated with gender (p = 0.005) and more pronounced among females. A majority (83%) of subjects expressed high levels of satisfaction with WB-MRI for early cancer diagnosis and judged it more acceptable than other diagnostic exams. CONCLUSIONS: Asymptomatic subjects self-referring to WB-MRI for early cancer diagnosis showed high levels of satisfaction and acceptability with the examination. Nevertheless, a relevant proportion of participants reported some form of discomfort. Interestingly, participants with higher perceived personal risk to get a disease, lower education and females showed to expect higher discomfort than experienced. ADVANCES IN KNOWLEDGE: Scope exists for measures to assess expected feelings and develop personalized interventions to reduce the stress anticipated by individuals deciding to undergo WB-MRI for early cancer diagnosis.
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Detección Precoz del Cáncer/métodos , Detección Precoz del Cáncer/psicología , Conocimientos, Actitudes y Práctica en Salud , Imagen por Resonancia Magnética/psicología , Satisfacción del Paciente/estadística & datos numéricos , Imagen de Cuerpo Entero/psicología , Adulto , Anciano , Escolaridad , Femenino , Humanos , Imagen por Resonancia Magnética/métodos , Imagen por Resonancia Magnética/estadística & datos numéricos , Masculino , Persona de Mediana Edad , Derivación y Consulta , Factores Sexuales , Encuestas y Cuestionarios , Imagen de Cuerpo Entero/métodos , Imagen de Cuerpo Entero/estadística & datos numéricosRESUMEN
BACKGROUND: The number of studies describing the use of whole-body magnetic resonance imaging (WB-MRI) for screening of malignant tumours in asymptomatic subjects is increasing. Our aim is to review the methodologies used and the results of the published studies on per patient and per lesion analysis, and to provide recommendations on the use of WB-MRI for cancer screening. MAIN BODY: We identified 12 studies, encompassing 6214 WB-MRI examinations, which provided the rates of abnormal findings and findings suspicious for cancer in asymptomatic subjects, from the general population. Eleven of 12 studies provided imaging protocols that included T1- and T2-weighted sequences, while only five included diffusion weighted imaging (DWI) of the whole body. Different categorical systems were used for the classification and the management of abnormal findings. Of 17,961 abnormal findings reported, 91% were benign, while 9% were oncologically relevant, requiring further investigations, and 0.5% of lesions were suspicious for cancer. A per-subject analysis showed that just 5% of subjects had no abnormal findings, while 95% had abnormal findings. Findings requiring further investigation were reported in 30% of all subjects, though in only 1.8% cancer was suspected. The overall rate of histologically confirmed cancer was 1.1%. CONCLUSION: WB-MRI studies of cancer screening in the asymptomatic general population are too heterogeneous to draw impactful conclusions regarding efficacy. A 5-point lesion scale based on the oncological relevance of findings appears the most appropriate for risk-based management stratification. WB-MRI examinations should be reported by experienced oncological radiologists versed on WB-MRI reading abnormalities and on onward referral pathways.
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Imagen por Resonancia Magnética/métodos , Neoplasias/diagnóstico por imagen , Imagen de Cuerpo Entero/métodos , Detección Precoz del Cáncer/métodos , Humanos , Guías de Práctica Clínica como AsuntoRESUMEN
Clinical suspicion of Prostate Cancer (PCa) is largely based on increased prostate specific antigen (PSA) level and/or abnormal digital rectal examination (DRE) and/or positive imaging and, up today, biopsy is mandatory to confirm the diagnosis. The old model consisted of Standard Biopsy (SBx), that is random sampling of the prostate gland under ultrasound guidance (TRUS), in subjects with clinical suspicion of PCa. This involves the risk of not diagnosing a high percentage of tumors (up to 30%) and of an incorrect risk stratification. Multiparametric Magnetic Resonance Imaging (mpMRI) has transformed the diagnostic pathway of PCa, not only as an imaging method for detecting suspicious lesions, but also as an intraprocedural guidance for Target Biopsy (MRI-TBx), thus bridging the diagnostic gap. Several single and multicenter randomized trials, such as PROMIS, MRI first, PRECISION and that reported by Van der Leest et al. have confirmed the superiority of the "MRI pathway", consisting of mpMRI and MRI-TBx of suspicious lesions, over the "standard pathway" of SBx in all patients with elevated PSA and/or positive DRE. MRI-TBx appears to be advantageous in reducing the overall number of biopsies performed, as well as in reducing the diagnosis of clinically insignificant disease while maintaining or improving the diagnosis of clinically significant PCa (cs-PCa). Moreover, it shows a reduction in the diagnosis of ins-PCa, and therefore, of overdiagnosis, when using MRI-TBx without sacrificing performance in the diagnosis of cs-PCa.