Nonfluorodeoxyglucose-Avid Persistent Splenomegaly at Time of Transplantation Delays Neutrophil and Platelets Engraftment without Affecting Survival in Patients with Lymphomas Undergoing Allogeneic Hematopoietic Cell Transplantation.

It is unclear if persistent splenomegaly in the presence of a negative positron emission tomography (PET) scan before allogeneic hematopoietic cell transplantation (HCT) influences post-transplantation outcomes in patients with lymphoma. We retrospectively reviewed records of 152 patients who underwent allogeneic HCT for various lymphomas. Centralized review of pretransplantation computed tomography (CT) and PET images was performed. Spleen volume (SV) was measured using the freehand volume segmentation tool in AW Workstation software (General Electric, Waukesha, WI). Splenic index (SI) was calculated as a product of width, thickness, and length of the spleen. Normal SV was defined as SV < 314.5 cm3 and normal SI was defined as SI ≤ 480 cm3, as described in the literature. Among the study population, 42.8% received an allogeneic HCT from an HLA-matched related donor, 36.2% from a matched unrelated donor, 12.5% from a mismatched unrelated donor, and 8.6% received a double umbilical cord blood transplantation. Most (61.8%) received myeloablative conditioning. Median age at transplantation was 52 (range, 21 to 68) years. Pre-allogeneic HCT spleen CT and PET images were available on 88% and 70.3% patients, respectively. SV ranged from 90 cm3 to 4684 cm3 with a median of 290.5 cm3 and a mean of 400.3 cm3. SI calculation showed a range from 50.3 cm3 to 8276.4 cm3 with a median of 582.1 cm3 and a mean of 771.2 cm3. The majority of patients (83.1%) had PET-negative spleen before allogeneic transplantation. Engraftment was delayed in PET-negative patients with persistent splenomegaly, with median days to neutrophil engraftment of 17 versus 16 (P = .03) and median days to platelet engraftment of 16 versus 14 (P = .04) when using SV. However, persistent splenomegaly did not appear to impact progression-free survival (P = .11) or overall survival (P = .37). Splenomegaly in the setting of a PET-negative study before allogeneic HCT delays neutrophil and platelet engraftment but does not appear to affect survival. Future studies using registry data or larger multicenter studies would be required to evaluate the impact of splenomegaly and its fluorodeoxyglucose avidity on allogeneic HCT outcomes in specific subtypes of lymphomas.

Diffuse Alveolar Hemorrhage in Acute Myeloid Leukemia.

Diffuse alveolar hemorrhage is a potentially fatal pulmonary disease syndrome that affects individuals with hematological and nonhematological malignancies. The range of inciting factors is wide for this syndrome and includes thrombocytopenia, underlying infection, coagulopathy, and the frequent use of anticoagulants, given the high incidence of venous thrombosis in this population. Dyspnea, fever, and cough are commonly presenting symptoms. However, clinical manifestations can be variable. Obvious bleeding (hemoptysis) is not always present and can pose a potential diagnostic challenge. Without prompt treatment, hypoxia that rapidly progresses to respiratory failure can occur. Diagnosis is primarily based on radiological and bronchoscopic findings. This syndrome is especially common in patients with hematological malignancies, given an even greater propensity for thrombocytopenia as a result of bone marrow suppression as well as the often prolonged immunosuppression in this patient population. The syndrome also has an increased incidence in individuals with hematological malignancies who have received a bone marrow transplant. We present a case series of 5 patients with acute myeloid leukemia presenting with diffuse alveolar hemorrhage at our institution. A comparison of clinical manifestations, radiographic findings, treatment course, and outcomes are described. A review of the literature and general overview of the diagnostic evaluation, differential diagnoses, pathophysiology, and treatment of this syndrome are discussed.

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration.

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

Pretherapy Ferumoxytol-enhanced MRI to Predict Response to Liposomal Irinotecan in Metastatic Breast Cancer.

Purpose To investigate ferumoxytol (FMX)-enhanced MRI as a pretreatment predictor of response to liposomal irinotecan (nal-IRI) for thoracoabdominal and brain metastases in women with metastatic breast cancer (mBC). Materials and Methods In this phase 1 expansion trial (ClinicalTrials.gov identifier, NCT01770353; 27 participants), 49 thoracoabdominal (19 participants; mean age, 48 years ± 11 [SD]) and 19 brain (seven participants; mean age, 54 years ± 8) metastases were analyzed on MR images acquired before, 1-4 hours after, and 16-24 hours after FMX administration. In thoracoabdominal metastases, tumor transverse relaxation rate (R*2) was normalized to the mean R*2 in the spleen (rR*2), and the tumor histogram metric rR*2,N, representing the average of rR*2 in voxels above the nth percentile, was computed. In brain metastases, a novel compartmentation index was derived by applying the MRI signal equation to phantom-calibrated coregistered FMX-enhanced MRI brain scans acquired before, 1-4 hours after, and 16-24 hours after FMX administration. The fraction of voxels with an FMX compartmentation index greater than 1 was computed over the whole tumor (FCIGT1) and from voxels above the 90th percentile R*2 (FCIGT1 R*2,90). Results rR*2,90 computed from pretherapy MRI performed 16-24 hours after FMX administration, without reference to calibration phantoms, predicted response to nal-IRI in thoracoabdominal metastases (accuracy, 74%). rR*2,90 performance was robust to the inclusion of some peritumoral tissue within the tumor region of interest. FCIGT1 R*2,90 provided 79% accuracy on cross-validation in prediction of response in brain metastases. Conclusion This first in-human study focused on mBC suggests that FMX-enhanced MRI biologic markers can be useful for pretherapy prediction of response to nal-IRI in patients with mBC. Keywords: MRI Contrast Agent, MRI, Breast, Head/Neck, Tumor Response, Experimental Investigations, Brain/Brain Stem Clinical trial registration no. NCT01770353 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Daldrup-Link in this issue.

A pilot study to evaluate tissue- and plasma-based DNA driver mutations in a cohort of patients with pancreatic intraductal papillary mucinous neoplasms.

Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions to pancreatic ductal adenocarcinoma that are challenging to manage due to limited imaging, cytologic, and molecular markers that accurately classify lesions, grade of dysplasia, or focus of invasion preoperatively. The objective of this pilot study was to determine the frequency and type of DNA mutations in a cohort of surgically resected, pathologically confirmed IPMN, and to determine if concordant mutations are detectable in paired pretreatment plasma samples. Formalin-fixed paraffin-embedded (FFPE) tissue from 46 surgically resected IPMNs (31 low-grade, 15 high-grade) and paired plasma from a subset of 15 IPMN cases (10 low-grade, 5 high-grade) were subjected to targeted mutation analysis using a QIAseq Targeted DNA Custom Panel. Common driver mutations were detected in FFPE from 44 of 46 (95.6%) IPMN cases spanning all grades; the most common DNA mutations included: KRAS (80%), RNF43 (24%), and GNAS (43%). Of note, we observed a significant increase in the frequency of RNF43 mutations from low-grade to high-grade IPMNs associated or concomitant with invasive carcinoma (trend test, P = 0.01). Among the subset of cases with paired plasma, driver mutations identified in the IPMNs were not detected in circulation. Overall, our results indicate that mutational burden for IPMNs is a common occurrence, even in low-grade IPMNs. Furthermore, although blood-based biopsies are an attractive, noninvasive method for detecting somatic DNA mutations, the QIAseq panel was not sensitive enough to detect driver mutations that existed in IPMN tissue using paired plasma in the volume we were able to retrieve for this retrospective study.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI.

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases.

Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.

Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report.

OBJECTIVES: Immune-checkpoint blockades have exhibited durable responses and improved long-term survival in a subset of advanced non-small cell lung cancer (NSCLC) patients. However, highly predictive markers of positive and negative responses to immunotherapy are a significant unmet clinical need. The objective of this study was to identify clinical and computational image-based predictors of rapid disease progression phenotypes in NSCLC patients treated with immune-checkpoint blockades. MATERIALS AND METHODS: Using time-to-progression (TTP) and/or tumor growth rates, rapid disease progression phenotypes were developed including hyperprogressive disease. The pre-treatment baseline predictors that were used to identify these phenotypes included patient demographics, clinical data, driver mutations, hematology data, and computational image-based features (radiomics) that were extracted from pre-treatment computed tomography scans. Synthetic Minority Oversampling Technique (SMOTE) was used to subsample minority groups to eliminate classification bias. Patient-level probabilities were calculated from the final clinical-radiomic models to subgroup patients by progression-free survival (PFS). RESULTS: Among 228 NSCLC patients treated with single agent or double agent immunotherapy, we identified parsimonious clinical-radiomic models with modest to high ability to predict rapid disease progression phenotypes with area under the receiver-operator characteristics ranging from 0.804 to 0.865. Patients who had TTP < 2 months or hyperprogressive disease were classified with 73.41% and 82.28% accuracy after SMOTE subsampling, respectively. When the patient subgroups based on patient-level probabilities were analyzed for survival outcomes, patients with higher probability scores had significantly worse PFS. CONCLUSIONS: The models found in this study have potential important translational implications to identify highly vulnerable NSCLC patients treated with immunotherapy that experience rapid disease progression and poor survival outcomes.