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- Editorial for “Three‐dimensional multifrequency MR elastography for microvascular invasion and prognosis assessment in Hepatocellular Carcinoma”Publication . Adubeiro, Nuno; Nunes, Rita G.; Nogueira, LuísaThe prognosis of individuals with hepatocellular carcinoma(HCC), the most prevalent primary liver malignancy, is closely linked to the aggressiveness and recurrence of the tumor. The occurrence of complications after surgery continues to be a major and persistent challenge. MR elastography (MRE) employs a modified phase-contrast imaging sequence, combined with the use of an external driver to transmit mechanical vibrations to the tissues, to identify propagating shear waves within the liver. This technique allows the assessment of a substantial portion of the liver and provides information on multiple mechanical properties associated with various pathophysiological states. Due to substantial progress in MR technology, MRE has proven to be a precise noninvasive diagnostic method for detecting and monitoring various liver diseases. MRE imaging could serve as a valuable tool for staging malignancy and predict disease prognosis.
- Apparent diffusion coefficient in the analysis of prostate cancerPublication . Adubeiro, Nuno; Nogueira, Luísa; Ribeiro, Eduardo; Alves, Sandra; Ferreira, Hugo; La Fuente, JoséThe multiparametric magnetic resonance imaging (MPMRI) approach, has allowed the diagnostic performance in the detection and characterization of prostate cancer (PCa). Diffusion-weighted imaging (DWI), is an important technique in the MPMRI, that provides qualitative and quantitative biological information regarding water diffusivity in a non-invasive manner. The apparent diffusion coefficient (ADC) measures water mobility and can be quantified from the signal intensity loss between two or more b-values. Different studies reported that ADC values are directly associated with microvessel density and cellularity. One of the main aspects that is in discussion is the b-values that must be used in the DWI sequence in order to compute ADC.
- Editorial for “Detecting adverse pathology of prostate cancer with a deep learning approach based on a 3D swin-transformer model and biparametric MRI: A multicenter retrospective study"Publication . Adubeiro, Nuno; Nogueira, LuísaProstate cancer (PCa) is the second most prevalent cancer among men worldwide. Timely and accurate diagnosis is important to avoid overtreatment of men with indolent, clinically insignificant PCa and to offer radical curative treatment with life-threatening, clinically significant PCa. Radical prostatectomy (RP) has become the standard care for eligible patients because of its cancer control and improved survival. Although most patients remained disease-free after RP, 20%–30% of patients develop recurrence of the disease at follow-up.3 Therefore, the assessment of reliable prognostic predictors of recurrence after RP is clinically important for guiding clinical decision-making and patient counseling. To date, several factors are considered adverse pathology (AP) features such as preoperative prostate-specific antigen (PSA) levels, Gleason score, tumor stage, surgical margin status, lymph node invasion, extracapsular extension (ECE), and seminal vesicle invasion (SVI). All of them have been identified as prognostic factors for recurrence after RP.
- Genetic variants at the Wnt/[beta]-catenin and oestrogen receptor signalling pathways are associated with low bone mineral density in dancersPublication . Amorim, Tânia; Durães, Cecília; Maia, José; Machado, José Carlos; Nogueira, Luísa; Adubeiro, Nuno; Flouris, Andreas D.; Metsios, George S.; Marques, Franklim; Wyon, Matthew; Koutedakis, YiannisResearch suggests that dancers are at higher risk of developing low bone mineral density (BMD) compared with the general population. However, the associated factors contributing to low BMD in dancers are not fully understood. We aimed to assess the association of single-nucleotide polymorphisms (SNPs) in the Wnt/b-catenin and oestrogen receptor (ER) signalling pathways with low BMD in dancers.
- Impact of walking on knee articular cartilage T2 values estimated with a dictionary-based approach - A pilot studyPublication . Coelho, José M.; Fernandes, T.T.; Alves, Sandra Maria; Nunes, R.G.; Nogueira, Luísa; Oliveira, A.Walking is crucial for knee articular cartilage (KAC) health. Routine MRI sequences lack sensitivity for early cartilage changes, and the use of parametric T2 maps to study the effect of walking on KAC composition is limited. This study aimed to evaluate if quantitative T2 maps using an Echo Modulation Curve (EMC) matching algorithm can detect KAC T2 variations due to water content changes after walking. Seven asymptomatic volunteers (3 females, 4 males, mean age 28.3 years) without knee pathologies participated. Sagittal knee MRI scans were performed before and after a 9-min treadmill walk using a Modified Bruce protocol. T2-weighted Multi-Echo Spin-Echo KAC images were acquired at 3T. Tibiofemoral cartilage was segmented semi-automatically on three slices per knee, defining 39 KAC samples. Quantitative T2 maps were created using a dictionary-matching algorithm. Paired t-tests assessed exercise impact on KAC T2 values, independent t-tests compared group differences, and Friedman test with Bonferroni correction evaluated regional T2 changes. Walking increased KAC T2 values (mean difference (md) 0.61 ± 1.71 ms; p ¼ 0.016). Significant differences were observed in “normal” BMI group (md 0.69 ± 1.27 ms; p ¼ 0.021). Regional analysis revealed significant differences in medial femur in males (md 0.9 ± 2.1 ms; p ¼ 0.049) and lateral tibia in females (md 1.4 ± 2.5 ms; p ¼ 0.046). The medial tibia showed significant differences across sub-regions (p ¼ 0.026). Quantitative T2 maps using the EMC matching algorithm detected consistent changes in KAC T2 values after a short walking period. Implications for practice: EMC quantitative T2 maps effectively detected knee cartilage changes postwalking. This technique could improve cartilage hydration assessments, aiding early detection in atrisk patients. It also suggests potential for personalized monitoring and rehabilitation, advancing musculoskeletal imaging and non-invasive joint health monitoring.
- Associations between nutrition, energy expenditure and energy availability with bone mass acquisition in dance students: a 3-year longitudinal studyPublication . Amorim, Tânia; Freitas, Laura; Metsios, George S.; Thayse, Natacha Gomes; Wyon, Matthew; Flouris, Andreas D.; Maia, José; Marques, Franklim; Nogueira, Luísa; Adubeiro, Nuno; Koutedakis, YiannisThree years of study showed that female and male vocational dancers displayed lower bone mass compared to controls, at forearm, lumbar spine and femoral neck. Energy intake was found to positively predict bone mass accruals only in female dancers at femoral neck. Vocational dancers can be a risk population to develop osteoporosis.
- Editorial for “3D breast cancer segmentation in DCE‐MRI using deep learning with weak annotation”Publication . Nogueira, Luísa; Adubeiro, Nuno; Nunes, Rita G.Magnetic resonance imaging (MRI) shows higher diagnostic performance in the detection of breast tumors, compared with other imaging modalities. Breast MRI protocols include dynamic contrast-enhanced (DCE) images with high spatial and temporal resolution and are central indiagnosis, staging, and follow-up of breast cancer. DCE features provide physiological and anatomical lesion characteristics. To extract these data, manual lesion segmentation is currently performed, which is a critical time-consuming step,introducing bias and variability and impacting the reproducibility of the extracted features. To overcome these limitations, artificial intelligence algorithms have been explored, especially deep learning (DL) methods, for automatic lesion segmentation. This has been an active area of research, pivotal in the analysis of quantitative medical images. Most lesion segmentation methods have been based on semiautomatic or supervised learning approaches, presenting an important limitation: slice-by-slice 2D segmentations are typically performed, leading to suboptimal 3D masks upon concatenation. Recently, DL methods based on vision transformers have gained popularity in breast lesion segmentation, improving results over traditional machine learning. Although fully convolutional neural networks (CNNs) show powerful learning capabilities, their performance in learning long-range dependencies is limited, presenting decreased capacity in the segmentation of structures including different shapes and scales. UNETR is an architecture that replaces the CNN-based encoder with a transformer, which can capture low-level details in 3D segmentation. UNETR directly connects the encoder to the decoder via skip connections and can directly use volumetric data. Compared with CNN or transformer-based segmentation methods, UNETR can better capture dependencies at diverse spatial scales, both local and long-range enabling improved segmentation. In this retrospective study, Kim et al developed a model based on weak annotations, for detection and 3D segmentation of breast cancer in a sample of 736 women, using different input combinations in a three-time point (3TP) approach, from DCE-MRI images, acquired in two 3 T scanners from different manufacturers. The sample was divided into training (N = 544)and test sets (N = 192). To reduce the workload required toobta in ground truth segmentations, tumors were first segmented using weak annotations by two radiologists in consensus drawing bounding rectangles encompassing the lesion on two projection images. The rectangles were used to generate a 3D bounding box applied to the image obtained by subtracting the pre-contrast from the post-contrast image. An automatic thres holding method was used for automatic lesion segmentation; the mask was then refined to better define the lesion boundaries and exclude noisyor confounding regions (false positives). For training the segmen-tation network, images acquired at three different temporal acquisition points (pre-contrast, early, and delayed post-contrast) were used to construct three inputs: input 1 (pre-contrast, early phase),input 2 (pre-contrast, early, and delayed phase), and input 3 (pre-contrast and delayed phase). A different UNETR model wastrained for each input, and segmentation performances were compared, qualitatively and quantitatively, based on MRI features and immunohistochemical (IHC) classification. The best DL model presented a reliable performance for automated 3D segmentation of breast cancer with a median dicesimilarity coefficient (DSC) of 0.75 for the whole breast and 0.89 for the index lesion. The performance of the UNETR model was in accordance with the DSC values reported by other researchers employing alternative segmentation algorithms. Regarding the qualitative analysis of the segmentation results, the segmentation was successfully done in 83% of the cases derived from inputs 1 and 2, and from these, 95% were considered as acceptable detection. The authors also evaluated the performance of the segmentation according to base line characteristics and found significant differences for the whole breast and main lesion. For main lesion, significant differences were observed according to lesion size and IHC type. Regarding visual analysis, significant differences were found between lesion type (mass vs. non-mass enhancement) and background parenchymal enhancement (BPE) level. In their study, there were nine cases of failed segmentation, which corresponded totumors with small volumes, from which five cases were not segmented and four cases corresponded to abundant BPE,meaning false-positive results.© 2023 International Society for Magnetic Resonance in Medicine. 2263 Further developments of 3D UNETR architecture could be done to improve small lesion detection, to distinguish between mass and non-mass lesions, especially the boundaries of non-masses, and to distinguish between BPE and tumors. Attending to the implementation of DL algorithms in the clinical practice, this type of algorithm is expected to improve the detection of small lesions and the prediction of response to treatment, there by reducing the number of performed biopsies and, potentially, enabling the use of an abbreviated MRI pro-tocol, which would reduce MRI exam durations, improving patient comfort, and reducing costs.
- Editorial for "Feasibility of velocity-selective arterial spin labelling in breast cancer patients for non-contrast enhanced perfusion imaging"Publication . Nogueira, Luísa; Nunes, Rita GouveiaDynamic contrast-enhanced MRI (DCE-MRI) is currently considered an essential sequence in breast imaging since perfusion is a marker of tumor vascularity. Previ ous studies using ultrafast DCE-MRI demonstrated that lesion perfusion is associated with its type, grade, histomorphology, and prognosis.1,2 Unfortunately, both standard and ultrafast DCE-MRI require injecting a gadolinium-based contrast agent, with all the associated pitfalls (eg, possible nephrotoxicity, higher costs, patient discomfort, and concerns of contrast retention); developing an alternative method for evaluating perfusion would therefore have a tremendous clinical impact.