The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine.

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Malignant Pleural Mesothelioma: Staging and Radiological Response Criteria in Patients Treated with Immune Checkpoint Inhibitors.

Malignant pleural mesothelioma (MPM) is a rare and challenging cancer associated with asbestos fiber exposure, which offers limited treatment options. Historically, platinum-based chemotherapy has been the primary approach, but recent developments have introduced immunotherapy as a promising alternative for the treatment of this disease. Nevertheless, the unique growth patterns and occasionally ambiguous progressive characteristics of MPM make the interpretation of radiological assessments complex. Immunotherapy further complicates matters by introducing unconventional treatment response patterns such as hyperprogression and pseudoprogression. Consequently, there is a growing imperative to integrate the standard RECIST criteria with the mesothelioma-specific mRECIST criteria (version 1.1), as outlined in iRECIST. This comprehensive review is driven by the intent to provide a valuable resource for radiologists and clinicians engaged in the diagnosis, treatment, and monitoring of MPM in the era of immunotherapy. Specifically, the current imaging methods employed for staging and follow-up will be exposed and discussed, with a focus on the technical specificities and the mRECIST 1.1 methodology. Furthermore, we will provide a discussion about major clinical trials related to the use of immunotherapy in MPM patients. Finally, the latest advancements in radiomics, the applications of artificial intelligence in MPM, and their potential impact on clinical practice for prognosis and therapy, are discussed.

The impact of COVID-19 on cancer patients.

The COVID-19 pandemic poses a significant challenge for individuals with compromised immune systems, such as patients with cancer, as they face a heightened susceptibility to severe infections compared to the general population. Such severe infections substantially increase the risk of morbidity and mortality among these patients. Notable risk factors for mortality include advanced age (> 70 years), current or past smoking history, advanced disease stage, the use of cytotoxic chemotherapy, and an Eastern Cooperative Oncology Group (ECOG) score of 2 or higher. Multiple types of vaccines have been developed and implemented, demonstrating remarkable efficacy in preventing infections. However, there have been observable reductions in their ability to elicit an immune response, particularly among individuals with hematological malignancies. The situation becomes more challenging due to the emergence of viral variants of concern (VOCs). Despite the increase in neutralizing antibody levels after vaccination, they remain lower in response to these evolving variants. The need for booster vaccinations has become apparent, particularly for this vulnerable population, due to the suboptimal immune response and waning of immunity post-vaccination. Examining and comprehending how the immune system reacts to various vaccination regimens for SARS-CoV-2 and its VOCs in cancer patients is crucial for designing clinical and public health strategies. This review aims to provide an updated overview of the effectiveness of COVID-19 vaccines in cancer patients, including those undergoing treatments such as hematopoietic stem cell transplantation (HCT) or chimeric antigen receptor (CAR) T cell therapy, by exploring the extent of both humoral and cellular immune responses to COVID-19 vaccination. Furthermore, it outlines risk factors and potential biomarkers that are associated with severe SARS-CoV-2 infection and vaccine responses, and offers suggestions for improving SARS-CoV-2 protection in cancer patients.

A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation.

BACKGROUND: Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes. OBJECTIVE: The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform. METHODS: Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay. RESULTS: Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist. CONCLUSIONS: This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.

Primary adrenal insufficiency induced by immune checkpoint inhibitors: biological, clinical, and radiological aspects.

Immune checkpoint inhibitors (ICI) have become a cornerstone in medical oncology, with evolving therapeutic strategies and applications. These monoclonal antibodies, designed to enhance immune responses, have revealed a spectrum of immune-related adverse events (irAEs). While many irAEs exhibit favorable responses to corticosteroid or immunosuppressive therapy, most ICI-related endocrinopathies necessitate lifelong replacement therapy and pose significant clinical challenges. Adrenal insufficiency (AI), a noteworthy endocrine irAE, can manifest as primary AI (PAI) or secondary AI (SAI), resulting from adrenal or pituitary gland dysfunction, respectively. ICI-induced AI, albeit relatively infrequent, occurs in 1-2% of patients receiving single-agent anti-Programmed Death-1/Programmed Death-Ligand 1 (PD-1/PD-L1) or Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) therapies and in a higher range of 4-9% when ICIs are used in combinations. Recognizing and addressing ICI-induced PAI is crucial, as it often presents with acute and potentially life-threatening symptoms, especially considering the expanding use of ICI therapy. This review provides an updated overview of ICI-induced PAI, exploring its clinical, diagnostic, and radiological aspects.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study.

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study.

Introduction: Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters. Methods: The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment. Results: DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected. Discussion: The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.

Considerations on surgery invasiveness and response and toxicity patterns in classic palliative radiotherapy for acrometastases of the hand: a hint for a potential role of stereotactic body radiation therapy? A case report and literature review.

Background: The rarity of hand acrometastases hampers the consensus-building for their optimal management among the involved oncology professionals. In the current literature, demolitive surgery overcomes the use of palliative radiotherapy, which proved to be ineffective in more than 30% of cases treated with classic palliative dose schemes, carrying also a not negligible radiation-related adverse event rate. Against this background, stereotactic body radiation therapy (SBRT) could emerge as a well-balanced therapeutic option. Case summary: Here we describe the methods and outcomes of a SBRT treatment of a painful and function-limiting hand acrometastasis in a patient with a history of stage IIIB lung adenocarcinoma. We delivered a total dose of 30 Gy in five daily fractions to a soft-tissue metastasis abutting the fifth metacarpal bone through the SBRT protocol generally used for intracranial treatments. A few weeks later, the patient reported a clinical complete response with acrometastasis and pain disappearance, function recovery, and no significant toxicity. The acrometastasis was the first sign of an atypical cancer progression. Conclusions: SBRT for hand acrometastases is feasible and might have the best therapeutic profile among the currently available treatment options for this rare clinical scenario. Larger investigations are needed to confirm the present single-case experience.

Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network.

PURPOSE: The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS: Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS: The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS: The results show low variability of connectivity topological metrics across sites running a harmonised protocol.

A systematic review and meta-analysis on the optimal treatment duration of checkpoint inhibitoRS in solid tumors: The OTHERS study.

No clear evidence supports the advantage of fixed (up to two years (2yICI)) or continuous treatment (more than two years (prolonged ICI)) in cancer patients achieving stable disease or response on immune checkpoint inhibitors (ICIs). We performed a systematic review and meta-analysis of randomized controlled trials reporting the duration of ICIs (alone or in combination with standard of care (SoC)) across various solid tumors. Overall, we identified 28,417 records through database searching. Based on the eligibility criteria, 57 studies were identified for the quantitative synthesis, including 22,977 patients receiving ICIs (with or without SoC). Prolonged ICI correlated with better overall survival (OS) than 2yICI in patients with melanoma (HR:1.55; 95%CI: 1.22,1.98), while 2yICI-SoC led to better OS than prolonged ICI-SoC in patients with NSCLC (HR: 0.84; 95%CI: 0.68,0.89). Prospective randomized trials are needed to assess the most appropriate duration of ICIs. OBJECTIVE: No clear evidence supports the advantage of fixed (up to two years (2yICI)) or continuous treatment (more than two years (prolonged ICI)) in cancer patients achieving stable disease or response on immune checkpoint inhibitors (ICIs). Here, we assessed the optimal treatment duration for ICIs in solid tumors. CONCLUSIONS: Prolonged ICIs administration does not seem to improve the outcomes of patients with NSCLC an RCC.

Case report: Multiple disconnection patterns revealed by a multi-modal analysis explained behavior after a focal frontal damage.

Introduction: There is overwhelming evidence that focal lesions cause structural, metabolic, functional, and electrical disconnection of regions directly and indirectly connected with the site of injury. Unfortunately, methods to study disconnection (positron emission tomography, structural and functional magnetic resonance imaging, electroencephalography) have been applied primarily in isolation without capturing their interaction. Moreover, multi-modal imaging studies applied to focal lesions are rare. Case report: We analyzed with a multi-modal approach the case of a patient presenting with borderline cognitive deficits across multiple domains and recurrent delirium. A post-surgical focal frontal lesion was evident based on the brain anatomical MRI. However, we were able to acquire also simultaneous MRI (structural and functional) and [18F]FDG using a hybrid PET/MRI scan along with EEG recordings. Despite the focality of the primary anatomical lesion, structural disconnection in the white matter bundles extended far beyond the lesion and showed a topographical match with the cortical glucose hypometabolism seen both locally and remotely, in posterior cortices. Similarly, a right frontal delta activity near/at the region of structural damage was associated with alterations of distant occipital alpha power. Moreover, functional MRI revealed even more widespread local and distant synchronization, involving also regions not affected by the structural/metabolic/electrical impairment. Conclusion: Overall, this exemplary multi-modal case study illustrates how a focal brain lesion causes a multiplicity of disconnection and functional impairments that extend beyond the borders of the anatomical irrecoverable damage. These effects were relevant to explain patient's behavior and may be potential targets of neuro-modulation strategies.

Sex differences in functional brain networks involved in interoception: An fMRI study.

Interoception can be described as the ability to perceive inner body sensations and it is different between biological sex. However, no previous research correlated this ability with brain functional connectivity (FC) between males and females. In this study, we used resting-state functional magnetic resonance imaging to investigate FC of networks involved in interoception among males and females in a sample of healthy volunteers matched for age. In total, 67 participants (34 females, mean age 44.2; 33 males, mean age 37.2) underwent a functional MRI session and completed the Self-Awareness Questionnaire (SAQ) that tests the interoceptive awareness. To assess the effect of sex on scores obtained on the SAQ we performed a multivariate analysis of variance. A whole-brain seed-to-seed FC analysis was conducted to investigate the correlation between SAQ score and FC, and then to test differences in FC between males and females with SAQ score as a covariate. MANOVA revealed a significant difference in SAQ scores between males and females with higher values for the second ones. Also, significant correlations among interoception scores and FC in Salience network and fronto-temporo-parietal brain areas have been detected, with a sharp prevalence for the female. These results support the idea of a female advantage in the attention toward interoceptive sensations, suggesting common inter-network areas that concur to create the sense of self.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.

Background and objectives: The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature. Methods: A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves. Results: 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique. Conclusions: The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

Infections in lung cancer patients undergoing immunotherapy and targeted therapy: An overview on the current scenario.

Patients with a diagnosis of lung cancer are often vulnerable to infection, and the risk is increased by tumor-associated immunosuppression and the effects of the treatments. Historically, links between the risk of infection and cytotoxic chemotherapy due to neutropenia and respiratory syndromes are well established. The advent of tyrosine kinase inhibitors (TKIs) and immune-checkpoint inhibitors (ICIs) targeting the programmed cell death-1 (PD-1)/programmed cell death- ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte antigen-4 (CTLA-4) have changed the treatment paradigm for lung cancer patients. Our understanding of the risk of infections while administrating these drugs is evolving, as are the biological mechanisms that are responsible. In this overview, we focus on the risk of infection with the use of targeted therapies and ICIs, summarizing current evidence from preclinical and clinical studies and discussing their clinical implications.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Mapping myelin in white matter with T1-weighted/T2-weighted maps: discrepancy with histology and other myelin MRI measures.

The ratio of T1-weighted/T2-weighted magnetic resonance images (T1w/T2w MRI) has been successfully applied at the cortical level since 2011 and is now one of the most used myelin mapping methods. However, no reports have explored the histological validity of T1w/T2w myelin mapping in white matter. Here we compare T1w/T2w with ex vivo postmortem histology and in vivo MRI methods, namely quantitative susceptibility mapping (QSM) and multi-echo T2 myelin water fraction (MWF) mapping techniques. We report a discrepancy between T1w/T2w myelin maps of the human corpus callosum and the histology and analyse the putative causes behind such discrepancy. T1w/T2w does not positively correlate with Luxol Fast Blue (LFB)-Optical Density but shows a weak to moderate, yet significant, negative correlation. On the contrary, MWF is strongly and positively correlated with LFB, whereas T1w/T2w and MWF maps are weakly negatively correlated. The discrepancy between T1w/T2w MRI maps, MWF and histological myelin maps suggests caution in using T1w/T2w as a white matter mapping method at the callosal level. While T1w/T2w imaging may correlate with myelin content at the cortical level, it is not a specific method to map myelin density in white matter.