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Abstract Background Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. Graphical Abstract

We studied the incidence of acute graft versus host disease (GvHD) and its outcome in three consecutive time frames (year 2010), in 3,120 patients allografted in two transplant Centers between 1976 and 2020. The median age increased over the three periods from 32 to 42 to 54 years (p 0.001); post-transplant cyclophosphamide (PTCY) (RR 0.41, p

This study retrospectively compared long-term outcomes of nonmyeloablative/reduced intensity conditioning (NMC/RIC) allogeneic hematopoietic cell transplantation (allo-HCT) from a haploidentical family donor (haplo-HCT) using posttransplant cyclophosphamide (PTCy) with those of matched sibling donor (MSD) and matched unrelated donor (MUD) with or without T-cell depletion (TCD+/TCD-) in patients with relapsed diffuse large B-cell lymphoma (DLBCL). Adult patients with DLBCL who had undergone their first NMC/RIC allo-HCT between 2008 and 2015 were included. Recipients of haplo-HCT were limited to those receiving graft-versus-host disease (GVHD) prophylaxis with PTCy. GVHD prophylaxis in MSD was limited to calcineurin inhibitor (CNI)-based approaches without in vivo TCD, while MUD recipients received CNI-based prophylaxis with or without TCD. Outcome analyses for overall survival (OS) and progression-free survival (PFS), nonrelapse mortality (NRM), and disease relapse/progression were calculated. A total of 1438 patients (haplo, 132; MSD, 525; MUD TCD+, 403; and MUD TCD-, 378) were included. Patients with haplo donors were significantly older, had a better performance status and had more frequently received total body irradiation-based conditioning regimens and bone marrow grafts than MSD and MUD TCD+ or TCD-. 3-year OS, PFS, NRM and relapse/progression incidence after haplo-HCT was 46%, 38%, 22%, and 41%, respectively, and not significantly different from outcomes of matched donor transplants on multivariate analyses. Haplo-HCT was associated with a lower cumulative incidence of chronic GVHD compared with MSD, MUD TCD+/TCD-. NMC/RIC haplo-HCT with PTCy seems to be a valuable alternative for patients with DLBCL considered for allo-HCT but lacking a matched donor.

Abstract Background In clinical practice, there is the need to optimize imaging usage in MM patients. Accordingly, the aim of this paper was to develop a simple computed tomography (CT) scoring method for MM, able to shorten and simplify the interpretation time with good intra- and inter-reader reliability. This method, named MSBDS (Myeloma Spine and Bone Damage Score) was developed with the final aim to use standard total-body CT in the routine practice of MM centres as a complement of standard evaluations in patients undergoing stem cells transplantation. Methods We used a widely accepted consensus formation method and literature research during three structured face-to-face meetings specifically designed to combine opinions from a group of experts with proven experience in multiple myeloma care and/or musculoskeletal CT to facilitate the consensus on the field of study topics and the contents of the MSBDS score. Seven practical requisites for the MSBDS score were agreed. A total of 70 MM patients (mean age, 60 years ±9.2 [standard deviation]; range, 35–70 years) undergoing total-body CT was included to develop MSBDS scores. Patients data were already stored in the Radiological database for other Research studies IRB approved (054/2019). Readers to test the MSDMS were radiologists and clinicians involved in MM care or expert in bone damage scores with different level of experience in musculoskeletal and total body CT. Readers were blinded to the clinical data of the patients. Results The MSBDS scores based on the consensus work described above and literature analysis was finalized. MSBDS is based on an additive scale with assessment of a total body CT with the bone window one time and includes indicators of structural bone damage and instability or fracture risk. The total score is given by the sum of item scores for abnormalities detected. Its values range from 0 (minimum) to values > 10 where 10 is represented by high-risk patients. In high-risk patients immediate surgical or radiation oncologist consultation is suggested. Conclusions The MSBDS descriptive criteria are easy, highly reproducible and can be considered as a strong base for harmonizing total body CT interpretation in multiple myeloma patients undergoing stem cell transplantation.

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Immune checkpoint inhibitors (CI) have demonstrated clinical activity in Hodgkin Lymphoma (HL) patients relapsing after autologous stem cell transplantation (ASCT), although only 20% complete response (CR) rate was observed. The efficacy of CI is strictly related to the host immune competence, which is impaired in heavily pre-treated HL patients. Here, we aimed to enhance the activity of early post-ASCT CI (nivolumab) administration with the infusion of autologous lymphocytes (ALI). Twelve patients with relapse/refractory (R/R) HL (median age 28.5 years; range 18-65), underwent lymphocyte apheresis after first line chemotherapy and then proceeded to salvage therapy. Subsequently, 9 patients with progressive disease at ASCT received early post-transplant CI supported with four ALI, whereas 3 responding patients received ALI alone, as a control cohort. No severe adverse events were recorded. HL-treated patients achieved negative PET scan CR and 8 are alive and disease-free after a median follow-up of 28 months. Four patients underwent subsequent allogeneic SCT. Phenotypic analysis of circulating cells showed a faster expansion of highly differentiated NK cells in ALI plus nivolumab-treated patients as compared to control patients. Our data show anti-tumor activity with good tolerability of ALI + CI for R/R HL and suggest that this setting may accelerate NK cell development/maturation and favor the expansion of the “adaptive” NK cell compartment in patients with HCMV seropositivity, in the absence of HCMV reactivation.

Objective: To assess the reliability of the myeloma spine and bone damage score (MSBDS) across multiple readers with different levels of expertise and from different institutions. Methods: A reliability exercise, including 104 data sets of static images and complete CT examinations of patients affected by multiple myeloma (MM), was performed. A complementary imaging atlas provided detailed examples of the MSBDS scores, including low-risk and high-risk lesions. A total of 15 readers testing the MSBDS were evaluated. ICC estimates and their 95% confidence intervals were calculated based on mean rating (k = 15), absolute agreement, a two-way random-effects model and Cronbach’s alpha. Results: Overall, the ICC correlation coefficient was 0.87 (95% confidence interval: 0.79–0.92), and the Cronbach’s alpha was 0.93 (95% confidence interval: 0.94–0.97). Global inter- and intra-observer agreement among the 15 readers with scores below or equal to 6 points and scores above 6 points were 0.81 (95% C.I.: 0.72–0.86) and 0.94 (95% C.I.:0.91–0.98), respectively. Conclusion: We present a consensus-based semiquantitative scoring systems for CT in MM with a complementary CT imaging atlas including detailed examples of relevant scoring techniques. We found substantial agreement among readers with different levels of experience, thereby supporting the role of the MSBDS for possible large-scale applications. Significance and Innovations • Based on previous work and definitions of the MSBDS, we present real-life reliability data for quantitative bone damage assessment in multiple myeloma (MM) patients on CT. • In this study, reliability for the MSBDS, which was tested on 15 readers with different levels of expertise and from different institutions, was shown to be moderate to excellent. • The complementary CT imaging atlas is expected to enhance unified interpretations of the MSBDS between different professionals dealing with MM patients in their routine clinical practice.

Background: Adenovirus infection (ADVi) is an emergent complication in adult patients undergoing allogeneic hematopoietic stem cell transplantation (allo‐HSCT) and is associated with poor outcome. Available data on risk factors and optimal management of ADVi in adult allo‐HSCT recipients are limited, and recommendations on monitoring and pre‐emptive therapy are mainly based on pediatric data. Methods: In this single‐center, retrospective study, we reported all cases of positive ADV‐DNA from adult patients undergoing allo‐HSCT in the period 2014–2019. The study aimed to describe the incidence of ADVi at day +180 post‐transplant. Secondly to describe timing, clinical presentation, risk factors, and outcome of ADVi and to analyze the application of a screening strategy in our cohort. Results: In 445 allo‐HSCT recipients, the day +180 incidence was: 9% (39/445) for ADVi, 5% (24/445) for ADV viremia (ADVv), and 3% (15/445) for localized ADVi. The median time to ADVi was 65 (IQR 19; 94) days after HSCT. ADVv‐related mortality was 13% (3/24), all cases occurring with blood max‐ADV‐DNA > 10^3 cp/mL. Independent risk factors for ADVi were diagnosis of lymphoproliferative disease (p =.011) and acute graft‐versus‐host‐disease (p =.021). Conclusions: In our cohort, ADVi and ADVv were more frequent than previously reported. ADVv with max‐ADV‐DNA > 10^3 cp/mL was associated with ADV‐related mortality, thus careful monitoring and early initiation of treatment are advisable. [ABSTRACT FROM AUTHOR]

Human cancers exhibit phenotypic diversity that medical imaging can precisely and non-invasively detect. Multiple factors underlying innovations and progresses in the medical imaging field exert diagnostic and therapeutic impacts. The emerging field of radiomics has shown unprecedented ability to use imaging information in guiding clinical decisions. To achieve clinical assessment that exploits radiomic knowledge sources, integration between diverse data types is required. A current gap is the accuracy with which radiomics aligns with clinical endpoints. We propose a novel methodological approach that synergizes data volumes (images), tissue-contextualized information breadth, and network-driven resolution depth. Following the Precision Medicine paradigm, disease monitoring and prognostic assessment are tackled at the individual level by examining medical images acquired from two patients affected by intracranial ependymoma (with and without relapse). The challenge of spatially characterizing intratumor heterogeneity is tackled by a network approach that presents two main advantages: (a) Increased detection in the image domain power from high spatial resolution, (b) Superior accuracy in generating hypotheses underlying clinical decisions.

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann–Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.

Pleuroparenchymal fibroelastosis is characterized by upper lobes subpleural intra-alveolar fibrosis and elastosis with visceral pleural fibrosis, which may occur after allogenic haematopoietic stem-cell transplantation (HSCT). The longitudinal changes of lung function preceding this complication have not been described. We report the case of an adult woman undergoing allogeneic HSCT for Hodgkin's lymphoma. Pulmonary function tests evolved from normal, before transplantation, to a restrictive pattern with normal residual volume 3 months after transplantation, then to an obstructive pattern consistent with bronchiolitis obliterans 18 months after transplantation, and finally to a severe mixed pattern with preserved residual volume. Computed tomography showed the distinctive features of pleuroparenchymal fibroelastosis, confirmed by histology of specimen from apical resection after pneumothorax. This case report suggests that pleuroparenchymal fibroelastosis may occur after HSCT following bronchiolitis obliterans syndrome with a mixed (restrictive-obstructive) lung function pattern. Keywords: Haematopoietic stem-cell transplantation, Idiopathic pneumonia syndrome, Bronchiolitis obliterans syndrome, Pleuroparenchymal fibroelastosis, Lung restriction, Air trapping

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient’s outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease.

Background and objectives: In order to increase the accuracy of lytic lesion detection in multiple myeloma, a dedicated second-opinion interpretation of medical images performed by subspecialty musculoskeletal radiologists could increase accuracy. Therefore, the purpose of this study is to evaluate the added value (increased accuracy) of subspecialty second-opinion (SSO) consultations for Computed Tomography (CT) examinations in Multiple Myeloma (MM) patients undergoing stem cell transplantation on standard computed tomography with a focus on focal lesion detection. Materials and Methods: Approval from the institutional review board was obtained. This retrospective study included 70 MM consecutive patients (mean age, 62 years ± 11.3 (standard deviation); range, 35–88 years) admitted in the last six years. Pre-transplant total-body CT (reported by general radiologists) was the only inclusion criteria. Each of these CT examinations had a second-opinion interpretation by two experienced subspecialty musculoskeletal (MSK) radiologists (13 years of experience and 6 years of experience, mean: 9.5 years), experts in musculoskeletal radiology and bone image interpretation with a focus on lytic lesions. Results: Per lesion intra- and inter-observer agreement between the two radiologists was calculated with K statistics and the results were good (K = 0.67: Confidence Inteval (CI) 95%: 0.61–0.78). When the initial CT reports were compared with the re-interpretation reports, 46 (65%) of the 70 cases (95% CI: 37–75%) had no discrepancy. There was a discrepancy in detecting a clinically unimportant abnormality in 10/70 (14%) patients (95% CI: 7–25%) unlikely to alter patient care or irrelevant to further clinical management. A discrepancy in interpreting a clinically important abnormality was registered in 14/70 (21%) patients for focal lesions. The mean diameter of focal lesions was: 23 mm (95% CI: 5–57 mm). The mean number of focal lesions per patient was 3.4 (95% CI). Conclusions: subspecialty second-opinion consultations in multiple myeloma CT is more accurate to identify lesions, especially lytic lesions, amenable to influence patients’ care.

Treating disease according to precision health requires the individualization of therapeutic solutions as a cardinal step that is part of a process that typically depends on multiple factors. The starting point is the collection and assembly of data over time to assess the patient’s health status and monitor response to therapy. Radiomics is a very important component of this process. Its main goal is implementing a protocol to quantify the image informative contents by first mining and then extracting the most representative features. Further analysis aims to detect potential disease phenotypes through signs and marks of heterogeneity. As multimodal images hinge on various data sources, and these can be integrated with treatment plans and follow-up information, radiomics is naturally centered on dynamically monitoring disease progression and/or the health trajectory of patients. However, radiomics creates critical needs too. A concise list includes: (a) successful harmonization of intra/inter-modality radiomic measurements to facilitate the association with other data domains (genetic, clinical, lifestyle aspects, etc.); (b) ability of data science to revise model strategies and analytics tools to tackle multiple data types and structures (electronic medical records, personal histories, hospitalization data, genomic from various specimens, imaging, etc.) and to offer data-agnostic solutions for patient outcomes prediction; (c) and model validation with independent datasets to ensure generalization of results, clinical value of new risk stratifications, and support to clinical decisions for highly individualized patient management.