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.

Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning.

The reprogramming of cellular metabolism is a hallmark of cancer diagnosis and prognosis. Proton magnetic resonance spectroscopic imaging (MRSI) is a non-invasive diagnostic technique for investigating brain metabolism to establish cancer diagnosis and IDH gene mutation diagnosis as well as facilitate pre-operative planning and treatment response monitoring. By allowing tissue metabolism to be quantified, MRSI provides added value to conventional MRI. MRSI can generate metabolite maps from a single volume or multiple volume elements within the whole brain. Metabolites such as NAA, Cho and Cr, as well as their ratios Cho:NAA ratio and Cho:Cr ratio, have been used to provide tumor diagnosis and aid in radiation therapy planning as well as treatment assessment. In addition to these common metabolites, 2-hydroxygluterate (2HG) has also been quantified using MRSI following the recent discovery of IDH mutations in gliomas. This has opened up targeted drug development to inhibit the mutant IDH pathway. This review provides guidance on MRSI in brain gliomas, including its acquisition, analysis methods, and evolving clinical applications.

Breast cancer screening in average-risk women: towards personalized screening.

Breast cancer screening is widely recognized for reducing breast cancer mortality. The objective in screening is to diagnose asymptomatic early stage disease, thereby improving treatment efficacy. Screening recommendations have been widely debated over the past years and controversies remain regarding the optimal screening frequency, age to start screening, and age to end screening. While there are no new trials, follow-up information of randomized controlled trials has become available. The American College of Physicians recently issued a new guidance statement on screening for breast cancer in average-risk women, with similar recommendations to the U.S. Preventive Services Task Force and to European guidelines. However, these guidelines differ from those ofother American specialty societies. The variations reflect differences in the organizations' values, the metrics used to evaluate screening results, and the differences in healthcare organization (individualized or state-organized healthcare). False-positive rates and overdiagnosis of biologically insignificant cancer are perceived as the most important potential harms associated with mammographic screening; however, there is limited evidence on their actual consequences. Most specialty societies agree that physicians should offer mammographic screening at age 40 years for average-risk women and discuss its benefits and potential harms to achieve a personalized screening strategy through a shared decision-making process.

Intramammary lymph nodes: normal and abnormal multimodality imaging features.

Intramammary lymph nodes (IMLN) are one of the most common benign findings at screening mammography. However, abnormal IMLN features, such as diminished or absent hilum, thickened cortex, not circumscribed margins, increased size or interval change, warrants additional follow-up or pathologic analysis to exclude malignancy. Some benign inflammatory conditions may be associated with imaging-detected suspected abnormal IMLN, such as reactive hyperplasia and silicone-induced lymphadenopathy. In patients with known breast cancer, IMLN are a potential site of locoregional spread, which can change the prognosis and management. In some cases, initial breast carcinomas can also mimic IMLN. Breast radiologists must also be aware of the typical and atypical characteristics of IMLN to suggest further investigation when it is necessary.