Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow

Effectiveness and safety of filgotinib in rheumatoid arthritis: a real-life multicentre experience

Objectives: We investigated the effectiveness and safety of filgotinib in a real-life multicentre cohort of rheumatoid arthritis (RA) patients. Methods: RA patients were evaluated at baseline and after 12 and 24 weeks and were stratified based on previous treatments as biologic disease-modifying anti-rheumatic drug (bDMARD)-naive and bDMARD-insufficient responders (IR). Concomitant usage of methotrexate (MTX) and oral glucocorticoids (GC) was recorded. At each timepoint we recorded disease activity, laboratory parameters and adverse events. Results: 126 patients were enrolled. 15.8% were bDMARD-naive (G0), while 84% were bDMARD-IR (G1). In G0, 45% of patients were in monotherapy (G2) and 55% were taken MTX (G3). In G1, 50% of patients were in monotherapy (G4) and 50% used MTX (G5).A significant reduction in all parameters at 12 weeks was observed; in the extension to 24 weeks the significant reduction was maintained for patient global assessment (PGA), examiner global assessment (EGA), visual analogue scale (VAS) pain, VAS fatigue, disease activity score (DAS)28- C-reactive protein (CRP) and CRP values. Filgotinib in monotherapy showed better outcomes in bDMARD-naive patients, with significant differences for patient reported outcomes (PROs) and DAS28-CRP. At 12 weeks, low disease activity (LDA) and remission were achieved in a percentage of 37.2 % and 10.7 % by simplified disease activity index (SDAI), 42.6 % and 5.7 % by clinical disease activity index (CDAI), 26.8 % and 25.2 % by DAS28-CRP, respectively. A significant decrease in steroid dose was evidenced in all patients. We observed a major adverse cardiovascular event in one patient and an increase in transaminase in another. No infections from Herpes Zoster were reported. Conclusions: Our real-world data confirm the effectiveness and safety of filgotinib in the management of RA, especially in bDMARD-naive patients

Validation of the Italian version of the ANCA-associated vasculitis patient-reported outcome (AAV-PRO) questionnaire

Objectives The primary objective of this study was the translation and validation of the ANCA-associated vasculitis patient-reported outcome (AAV-PRO) questionnaire into Italian, denoted as AAV-PRO_ita. The secondary objective was to evaluate the impact of ANCA-associated vasculitis (AAV) on quality of life (QoL) and work impairment in a large cohort of Italian patients. Methods The study design took a prospective cohort study approach. First, the AAV-PRO was translated into Italian following the step guidelines for translations. The new AAV-PRO_ita questionnaire covered three disease domains: organ-specific and systemic symptoms and signs; physical function; and social and emotional impact. Second, Italian-speaking AAV patients were recruited from 17 Italian centres belonging to the Italian Vasculitis Study Group. Participants completed the AAV-PRO_ita questionnaire at three time points. Participants were also requested to complete the work productivity and activity impairment: general health questionnaire. Results A total of 276 AAV patients (56.5% women) completed the questionnaires. The AAV-PRO_ita questionnaire demonstrated a good internal consistency and test-retest reliability. Female AAV patients scored higher (i.e. worse) in all thee domains, especially in the social and emotional impact domain (P < 0.001). Patients on glucocorticoid therapy (n = 199) had higher scores in all domains, especially in the physical function domain (P < 0.001), compared with patients not on glucocorticoid therapy (n = 77). Furthermore, patients who had at least one relapse of disease (n = 114) had higher scores compared with those who had never had one (n = 161) in any domain (P < 0.05). Finally, nearly 30% of the patients reported work impairment. Conclusion The AAV-PRO_ita questionnaire is a new 29-item, disease-specific patient-reported outcome measuring tool that can be used in AAV research in the Italian language. Sex, glucocorticoids and relapsing disease showed the greatest impact on QoL

Clinical and laboratory features associated with macrophage activation syndrome in Still's disease: data from the international AIDA Network Still's Disease Registry

: To characterize clinical and laboratory signs of patients with still's disease experiencing macrophage activation syndrome (MAS) and identify factors associated with MAS development. patients with still's disease classified according to internationally accepted criteria were enrolled in the autoInflammatory disease alliance (AIDA) still's disease registry. clinical and laboratory features observed during the inflammatory attack complicated by MAS were included in univariate and multivariate logistic regression analysis to identify factors associated to MAS development. A total of 414 patients with Still's disease were included; 39 (9.4%) of them developed MAS during clinical history. At univariate analyses, the following variables were significantly associated with MAS: classification of arthritis based on the number of joints involved (p = 0.003), liver involvement (p = 0.04), hepatomegaly (p = 0.02), hepatic failure (p = 0.01), axillary lymphadenopathy (p = 0.04), pneumonia (p = 0.03), acute respiratory distress syndrome (p < 0.001), platelet abnormalities (p < 0.001), high serum ferritin levels (p = 0.009), abnormal liver function tests (p = 0.009), hypoalbuminemia (p = 0.002), increased LDH (p = 0.001), and LDH serum levels (p < 0.001). at multivariate analysis, hepatomegaly (OR 8.7, 95% CI 1.9-52.6, p = 0.007) and monoarthritis (OR 15.8, 95% CI 2.9-97.1, p = 0.001), were directly associated with MAS, while the decade of life at Still's disease onset (OR 0.6, 95% CI 0.4-0.9, p = 0.045), a normal platelet count (OR 0.1, 95% CI 0.01-0.8, p = 0.034) or thrombocytosis (OR 0.01, 95% CI 0.0-0.2, p = 0.008) resulted to be protective. clinical and laboratory factors associated with MAS development have been identified in a large cohort of patients based on real-life data

Combining molecular and imaging metrics in cancer: radiogenomics.

BACKGROUND: Radiogenomics is the extension of radiomics through the combination of genetic and radiomic data. Because genetic testing remains expensive, invasive, and time-consuming, and thus unavailable for all patients, radiogenomics may play an important role in providing accurate imaging surrogates which are correlated with genetic expression, thereby serving as a substitute for genetic testing. MAIN BODY: In this article, we define the meaning of radiogenomics and the difference between radiomics and radiogenomics. We provide an up-to-date review of the radiomics and radiogenomics literature in oncology, focusing on breast, brain, gynecological, liver, kidney, prostate and lung malignancies. We also discuss the current challenges to radiogenomics analysis. CONCLUSION: Radiomics and radiogenomics are promising to increase precision in diagnosis, assessment of prognosis, and prediction of treatment response, providing valuable information for patient care throughout the course of the disease, given that this information is easily obtainable with imaging. Larger prospective studies and standardization will be needed to define relevant imaging biomarkers before they can be implemented into the clinical workflow

Fibromatosis of the breast mimicking cancer: A case report

Breast fibromatosis, also referred to as desmoid tumor or aggressive fibromatosis, is a very rare, locally aggressive disease that does not metastasize. Bilateral lesions are extremely rare and are found in only 4% of patients with breast fibromatosis. Tumor recurrence following surgery occurs in 18%-29% of patients, most often within the first 2 years after surgery. In this report, we discuss a case of breast fibromatosis, mimicking a breast carcinoma both clinically and radiologically, that presented clinically with dimpling of the skin of the left breast in a 31-year-old woman. The patient relapsed a few months after surgery, with a multicentric and bilateral disease

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patients tumor on multiparametric MRI is insufficient to predict that patients response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation