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Intratumoral heterogeneity presents a major challenge to diagnosis and treatment of glioblastoma (GBM). Such heterogeneity is further exacerbated upon the recurrence of GBM, where treatment-induced reactive changes produce additional intratumoral heterogeneity that is ambiguous to differentiate on clinical imaging. There is an urgent need to develop non-invasive approaches to map the heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We propose to predictively fuse MRI with the underlying intratumoral heterogeneity in recurrent GBM using machine learning (ML) by leveraging unique image-localized biopsies with their associated locoregional MRI features. To this end, we develop BioNet, a biologically informed multi-task framework combining Bayesian neural networks and semi-supervised adversarial autoencoders, to predict regional distributions of three tissue-specific gene modules: proliferating tumor, reactive/inflammatory cells, and infiltrated brain tissue. BioNet provides insight into how to integrate implicit and hierarchical domain knowledge, which is difficult to incorporate into ML models through existing methods. The proposed architecture further addresses challenges in exploiting latent feature structures from limited labeled image-localized biopsy samples, which lead to improvements in prediction accuracy. BioNet performs significantly better than existing methods on cross-validation and blind test datasets, shows generalizability that surpasses other models, and is adaptable to different types of data or tasks. Prediction maps of gene modules from BioNet provide accurate predictions of intratumoral heterogeneity, which can improve surgical planning and localization of diagnostic biopsies, as well as inform neuro-oncological treatment assessment for each patient. These results also highlight the emerging role of ML in precision medicine.Significance StatementQuantitative assessments of intratumoral heterogeneity are limited by sparse biopsy sampling but is crucial for diagnosis, clinical management and treatment of (recurrent) glioblastoma. We propose leveraging a unique cohort of image-localized biopsies and their associated locoregional imaging features to develop a deep learning model, BioNet, that takes as input patient MRIs to predict output maps of the regional distributions of tissue-states. BioNet is able to (1) amplify the signal to noise ratio of the intratumoral genetic and cellular heterogeneity and (2) augment the learning capability of deep learning (DL) models through integrating implicit, hierarchical, but hard to be mathematically formulated domain knowledge. Our method performs significantly better than existing methods and is able to be adapted to related diseases.

Early Alzheimer’s disease (AD) pathology can be found in cortical biopsies taken during shunt placement for Normal Pressure Hydrocephalus. This represents an opportunity to study early AD pathology in living patients. Here we report RNA-seq data on 106 cortical biopsies from this patient population. A restricted set of genes correlate with AD pathology in these biopsies, and co-expression network analysis demonstrates an evolution from microglial homeostasis to a disease-associated microglial phenotype in conjunction with increasing AD pathologic burden, along with a subset of additional astrocytic and neuronal genes that accompany these changes. Further analysis demonstrates that these correlations are driven by patients that report mild cognitive symptoms, despite similar levels of biopsy β-amyloid and tau pathology in comparison to patients who report no cognitive symptoms. Taken together, these findings highlight a restricted set of microglial and non-microglial genes that correlate with early AD pathology in the setting of subjective cognitive decline., Specific transcriptional changes in microglia associated with Alzheimer’s disease have been reported. Here, the authors show that transcriptional analysis of human hydrocephalus biopsies identifies changes in immune response genes associated with early AD pathology, including cognitive decline.

Background Gliomas comprise the most common type of primary brain tumor, are highly invasive, and often fatal. IDH-mutated gliomas are particularly challenging to image and there is currently no clinically accepted method for identifying the extent of tumor burden in these neoplasms. This uncertainty poses a challenge to clinicians who must balance the need to treat the tumor while sparing healthy brain from iatrogenic damage. The purpose of this study was to investigate the feasibility of using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to detect glioma-related asynchrony in vascular dynamics for distinguishing tumor from healthy brain. Methods Twenty-four stereotactically localized biopsies were obtained during open surgical resection from ten treatment-naïve patients with IDH-mutated gliomas who received standard-of-care preoperative imaging as well as echo-planar resting-state BOLD fMRI. Signal intensity for BOLD asynchrony and standard-of-care imaging was compared to cell counts of total cellularity (H&E), tumor density (IDH1 & Sox2), cellular proliferation (Ki67), and neuronal density (NeuN), for each corresponding sample. Results BOLD asynchrony was directly related to total cellularity (H&E, P = 4 × 10–5), tumor density (IDH1, P = 4 × 10–5; Sox2, P = 3 × 10–5), cellular proliferation (Ki67, P = .002), and inversely related to neuronal density (NeuN, P = 1 × 10–4). Conclusions Asynchrony in vascular dynamics, as measured by resting-state BOLD fMRI, correlates with tumor burden and provides a radiographic delineation of tumor boundaries in IDH-mutated gliomas.

Objective To determine the risk of acquiring perioperative COVID-19 infection in previously COVID-19 negative patients. Summary of background data During the initial peak of the COVID-19 pandemic, there was significant concern of hospital acquired COVID-19 infections. Medical centers rapidly implemented systems to minimize perioperative transmission, including routine preoperative testing, patient isolation, and enhanced cleaning. Methods In this retrospective cohort study, medical records of all adult patients who underwent surgery at our quaternary, acute care hospital between March 15 and May 15, 2020 were reviewed. The risk of preoperatively negative patients developing symptomatic COVID-19 within 2-14 days postoperatively was determined. Surgical characteristics, outcomes, and complications were compared between those with and without acquired perioperative COVID-19 infection. Results Among 501 negative patients undergoing index surgeries, 9 (1.8%) developed symptomatic COVID-19 in the postoperative period; all occurred before implementation of routine preoperative testing [9/243, 3.7% vs 0/258, 0%, odds ratio (OR): 0.048, P = 0.036]. No patient who was polymerase-chain-reaction negative on the day of surgery (n = 170) developed postoperative infection. Perioperative infection was associated with preoperative diabetes (OR: 3.70, P = 0.042), cardiovascular disease (OR: 3.69, P = 0.043), angiotensin receptor blocker use (OR: 6.58, P = 0.004), and transplant surgery (OR: 11.00, P = 0.002), and multiple complications, readmission (OR: 5.50, P = 0.029) and death (OR: 12.81, P = 0.001). Conclusions During the initial peak of the COVID-19 pandemic, there was minimal risk of acquiring symptomatic perioperative COVID-19 infection, especially after the implementation of routine preoperative testing. However, perioperative COVID-19 infection was associated with poor postoperative outcome.

Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of 14 months. GBMs are challenging to treat due to their heterogeneous nature. It has also been seen that these tumors have sex differences in their cellular subtypes as well as imaging. Radiomics has the potential to provide a non-invasive, spatial understanding of genetic and epigenetic diversity in these complex tumors and to aid in treatment planning. We have an ongoing study to obtain image-localized biopsies from GBM patients, allowing us to complete radiomic analysis and make connections between immunohistochemistry (IHC) and magnetic resonance imaging (MRI) features. We sought to determine if the patterns on imaging were correlated with underlying tumor biology. We focused on immunohistochemistry (IHC) markers of key features of tumor biology including SOX2 for stem-like tumor cells, CD68 for immune response and Ki67 for proliferation kinetics. Our study included 38 patients with a total of 99 biopsies (bxs): 27 males with 77 bxs and 11 females with 22 bxs. Biopsies were sectioned and stained for the SOX2, CD68, and KI67 markers. We computed 18 first-order radiomic features at each biopsy location for patients’ multimodal MRIs: T1W, T1Gd, T2W, FLAIR, apparent diffusion coefficient, diffusion weighted imaging (DWI) and susceptibility weighted imaging. We then performed correlation analysis between each radiomic feature and marker abundance for each IHC stain. Overall, we found sex-distinct patterns connecting imaging with these IHC markers. For example, amongst female patients, DWI held more prominent correlations with SOX2 than in males. Whereas there were more correlations between CD68 IHC abundance and T1Gd imaging features in males compared to females. Taken together, the overall patterns connecting locoregional imaging features to these IHC markers showed sex-distinct patterns suggesting the potential for sex to be an important biological variable when interpreting the biology underlying imaging changes.

Glioblastoma (GBM) is an aggressive diffusely infiltrating neoplasm that spreads beyond surgical resection margins, where it intermingles with non-neoplastic brain cells. This complex microenvironment harboring infiltrating glioma and non-neoplastic brain cells is the origin of tumor recurrence. Thus, understanding the cellular and molecular features of the glioma microenvironment is therapeutically and prognostically important. We used single-nucleus RNA sequencing (snRNAseq) to determine the cellular composition and transcriptional states in primary and recurrent glioma and identified three compositional ‘tissue-states’ defined by the observed patterns of cohabitation between neoplastic and non-neoplastic brain cells. These comprise states enriched in A) neurons and non-neoplastic glia, B) reactive astrocytes and inflammatory cells, and C) proliferating tumor cells. The tissue states also showed distinct associations with the different transcriptional states of GBM cells. Spatial transcriptomics revealed that the cell-types/transcriptional-states associated with each tissue state colocalize in space. Tissue states are clinically significant because they correlate with radiographic, histopathologic, and prognostic features. Importantly, we found that our compositionally-defined tissue states are enriched in distinct metabolic pathways. One such pathway is fatty acid biosynthesis, which was enriched in tissue state B – a state enriched in recurrent glioblastoma and associated with shorter overall survival- and composed of astrocyte-like/mesenchymal glioma cells, reactive astrocytes, and monocyte-like myeloid cells. We showed that treating acute slices of GBM with a fatty acid synthesis inhibitor is sufficient to deplete the transcriptional signature of tissue state B. Our findings define a novel compositional approach to analyze glioma-infiltrated tissue which allows us to discover prognostic and targetable features, paving the way to new mechanistic and therapeutic discoveries.

BACKGROUND: Prior studies have highlighted infratentorial tumor location as a prognostic factor for solitary fibrous tumor (SFT) and hemangiopericytoma (HPC) of the central nervous system (CNS), and spinal location is considered a positive prognostic factor for other tumors of the CNS. While SFT/HPC of the CNS is known to frequently arise from the spinal meninges, there are no case series that report outcomes for spinally located CNS tumors, and their prognosis in relation to intracranial and other CNS-located tumors is unknown. OBJECTIVE: To investigate outcomes for patients with SFT/HPC of the spinal meninges. METHODS: The Surveillance, Epidemiology, and End-Results Program was used to identify patients with SFT/HPC within the CNS from 1993 – 2015. We retrospectively analyzed the relationship between tumor location (spinal vs. brain and other CNS) and survival. RESULTS: We identified 551 cases of CNS SFT/HPC, 64 (11.6%) of which were primary tumors of the spinal meninges. Spinal tumors were more likely than brain and other CNS tumors to be SFT vs. HPC (37.5 vs. 12%, p < 0.001), benign (42.2 vs. 20.3%, p < 0.001), and less than 5 cm (53.1 vs. 35.7%, p < 0.001). The 10-year survival rates for spinal and brain/other CNS tumors were 85 and 58%, respectively. Median survival time was significantly longer for spinal tumors (median survival not reached vs. 138 months, p = 0.03, HR = 0.41 [95% CI 0.18 – 0.94]). On multivariable analysis, spinal tumor location was associated with improved survival over tumors located in the brain and other CNS (HR = 0.36 [95% CI 0.15 – 0.89], p = 0.03). CONCLUSION: Spinal tumor location is associated with improved survival in patients with SFT/HPC of the CNS. Larger institutional studies are necessary to characterize the relationship between tumor location and other relevant factors such as presentation and amenability to gross-total resection and adjuvant radiotherapy. Future studies exploring optimal management of spinally located tumors are also needed.

Urogenital schistosomiasis remains a major global challenge. Optimal management of this infection depends upon imaging-based assessment of sequelae. Although established imaging modalities such as ultrasonography, plain radiography, magnetic resonance imaging (MRI), narrow band imaging, and computerized tomography (CT) have been used to determine tissue involvement by urogenital schistosomiasis, newer refinements in associated technologies may lead to improvements in patient care. Moreover, application of investigational imaging methods such as confocal laser endomicroscopy and two-photon microscopy in animal models of urogenital schistosomiasis are likely to contribute to our understanding of this infection's pathogenesis. This review discusses prior use of imaging in patients with urogenital schistosomiasis and experimentally infected animals, the advantages and limitations of these modalities, the latest radiologic developments relevant to this infection, and a proposed future diagnostic standard of care for management of afflicted patients.

Glioblastoma (GBM) is a devastating primary brain tumor known for its heterogeneity with a median survival of 15 months. Clinical imaging remains the primary modality to assess brain tumor response, but it is nearly impossible to distinguish between tumor growth and treatment response. Ki67 is a marker of active cell proliferation that shows inter- and intra-patient heterogeneity and should change under many therapies. In this work, we assessed the utility of a semi-supervised deep learning approach for regionally predicting high-vs-low Ki67 in GBM patients based on MRI. We used both labeled and unlabeled datasets to train the model. Labeled data included 114 MRI-localized biopsies from 43 unique GBM patients with available immunohistochemistry Ki67 labels. Unlabeled data included nine repeat routine pretreatment paired scans of newly-diagnosed GBM patients acquired within three days. Data augmentation techniques were utilized to enhance the size of our data and increase generalizability. Data was split between training, validation, and testing sets using 65-15-20 percent ratios. Model inputs were 16x16x3 patches around biopsies on T1Gd and T2 MRIs for labeled data, and around randomly selected patches inside the T2 abnormal region for unlabeled data. The network was a 4-conv layered VGG-inspired architecture. Training objective was accurate prediction of Ki67 in labeled patches and consistency in predictions across repeat unlabeled patches. We measured final model accuracy on held-out test samples. Our promising preliminary results suggest potential for deep learning in deconvolving the spatial heterogeneity of proliferative GBM subpopulations. If successful, this model can provide a non-invasive readout of cell proliferation and reveal the effectiveness of a given cytotoxic therapy dynamically during the patient's routine follow up. Further, the spatial resolution of our approach provides insights into the intra-tumoral heterogeneity of response which can be related to heterogeneity in localization of therapies (e.g. radiation therapy, drug dose heterogeneity).

Urogenital schistosomiasis remains a major global challenge. Optimal management of this infection depends upon imaging-based assessment of sequelae. Although established imaging modalities such as ultrasonography, plain radiography, magnetic resonance imaging (MRI), narrow band imaging, and computerized tomography (CT) have been used to determine tissue involvement by urogenital schistosomiasis, newer refinements in associated technologies may lead to improvements in patient care. Moreover, application of investigational imaging methods such as confocal laser endomicroscopy and two-photon microscopy in animal models of urogenital schistosomiasis are likely to contribute to our understanding of this infection's pathogenesis. This review discusses prior use of imaging in patients with urogenital schistosomiasis and experimentally infected animals, the advantages and limitations of these modalities, the latest radiologic developments relevant to this infection, and a proposed future diagnostic standard of care for management of afflicted patients.

Background Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs). Methods We sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations. Results While HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors. Conclusions These studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells. Electronic supplementary material The online version of this article (10.1186/s13073-018-0567-9) contains supplementary material, which is available to authorized users.

Introduction Distinguishing between tumor and treatment effect in post-treatment glioma, although crucial for clinical management, is difficult because contrast-enhancing regions are mixtures of recurrent tumor and reactive tissue, and definitive histopathological criteria do not exist. This study disentangles the marked intra-tumoral heterogeneity in the treatment-recurrent setting by developing an unsupervised framework to algorithmically categorize intraoperative MRI-localized biopsies into three clinically-relevant tissue clusters based on joint analysis of RNA sequencing and histopathological data. Methods A retrospective cohort of 84 MRI-localized biopsies from 37 patients with post-treatment recurrent glioblastoma underwent mRNA extraction and quantification via PLATEseq protocol. For 48 of 84 biopsies, a neighboring piece of tissue underwent quantitative histopathology based on labeling index (LI) for SOX2, CD68, NeuN, Ki67, and H&E. Correlation between LIs and gene expression for these 48 samples was performed. Genes significantly correlated (p Results Correlation analysis identified 7779 genes significantly correlated with ≥1 histopathological marker. Clustering revealed three gene sets associated with specific markers: SetA-3688 genes associated with SOX2/Ki67/H&E; SetB-2418 genes associated with CD68; SetC-1673 genes associated with NeuN. ssGSEA using these sets categorized each biopsy into one of three tissue types: 27 biopsies enriched in SetA, 28 in SetB, and 29 in SetC. Conclusions Using MRI-localized biopsies with both RNAseq and histopathological data, this algorithmic approach allows development of three orthogonal tissue-specific gene sets that can be applied to characterize the heterogeneity in post-treatment recurrent glioma: SetA: genes correlated with SOX2/Ki67/H&E, representing recurrent tumor; SetB: genes correlated with CD68 (microglia) representing reactive tissue consistent with treatment effect; SetC: genes correlated with NeuN (neurons), representing infiltrated brain.

Evaluation of recurrence in post-treatment glioma is challenging because contrast-enhancing (CE) lesions are a mixture of tumor and treatment effect. This study characterizes intratumoral heterogeneity using quantitative digital pathology to correlate intraoperative MRI-localized biopsies with histopathology in the post-treatment setting. Findings will inform multiparametric radiographic models of intratumoral heterogeneity. A retrospective review was performed on adult patients with MRI-localized biopsies obtained during resection for post-treatment recurrent high-grade glioma. 68 patients and 170 MRI-localized samples were analyzed (median 2 samples/patient). Immunohistochemistry (IHC) for markers of glioma cells (SOX2), macrophages (CD68), and proliferating cells (KI67) was used to characterize biopsies. Slides were digitized and quantified using an automated cell-counting algorithm. Histopathological criteria based on IHC data was developed to classify biopsies. IHC quantification was compared across histological groups using ANOVA and paired t-tests. Most patients (52/68) yielded multiple biopsies. 75% (39/52) demonstrated heterogeneity in histological classification of all specimens obtained from their lesion. 47/170 (28%) biopsies were predominantly treatment effect, and most were CE (31/47 or 66%). Only 75/170 (44%) biopsies contained recurrent glioma, and 21/75 (28%) were NE. SOX2 labeling index was higher in biopsies containing recurrent tumor (p=5.13E-25). CD68 labeling index was higher in biopsies with predominant treatment effect (p=1.35E-12). IHC data from MRI-localized biopsies informed a multiple linear regression model which demonstrated significant predictive value for determining the distribution of recurrent tumor in the post-treatment setting. Contrast enhancement is not a reliable predictor of tumor in recurrent high-grade glioma. Most patients demonstrated marked intratumoral heterogeneity, highlighting the difficulty of accurate tumor sampling post-treatment glioma. Our histopathological classification significantly distinguished recurrent tumor from treatment effect and informed a multiparametric radiomic model which can guide surgical sampling and assess response to therapy.

INTRODUCTION: Accurate intraoperative diagnosis is essential for providing optimal neurosurgical care. In many centers caring for brain tumor patients, neuropathology resources are limited. To augment existing neuropathology resources, we developed and validated a new paradigm combining optical histology and artificial intelligence (AI) to accurately predict diagnosis during brain tumor surgery. METHODS: A total of 1026 specimens from 501 patients undergoing brain tumor resection at two tertiary hospitals were imaged using an optical technique, called stimulated Raman histology (SRH). SRH images were used to train and validate a convolutional neural network (CNN) for state-of-the-art computer vision. We redesigned the GoogleNet InceptionV3 CNN architecture to optimize performance on SRH histologic image fields of view (FOVs) and trained the network using 466 patients (3.1 million unique 300m2 FOVs) to classify into 13 common brain tumor subtypes. Final intraoperative diagnosis was determined using the most commonly predicted FOV diagnosis within each specimen. Model testing was completed on 1 million unique FOVs from 35 prospectively enrolled patients whose data was not included in the training set. RESULTS: In the validation set, our trained CNN differentiated lesional from normal tissue with 100% accuracy, surgical from nonsurgical lesions with 100% accuracy, glial from non-glial tumors with 100% accuracy. When evaluating our model for tumor subtype classification, we achieved an accuracy of 97% (35/36 patients) compared to final clinical diagnosis. Corresponding clinical frozen section diagnostic accuracy was 97% and interrater agreement between CNN and clinical frozen section diagnosis was near-perfect (k>0.96). The sole CNN error was misclassification of a WHO grade 1 pilocytic astrocytoma as a WHO grade II astrocytoma. CONCLUSION: Our prospective, multi-institutional validation suggests that AI can be applied to predict diagnosis in neurosurgical specimens in an automated fashion. AI-based diagnosis may ultimately be used to augment the current neuropathology workflow where resources are limited.

BackgroundDespite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs). We sampled the cellular milieu of HGGs with massively-parallel single-cell RNA-Seq.ResultsWhile HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.ConclusionsThese studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

High-grade gliomas (HGGs) nearly always recur after standard initial treatment, and the resulting mixture of recurrent tumor and treatment-induced reactive changes presents major diagnostic challenges. Anatomical imaging, such as MRI, cannot adequately distinguish progressive disease from treatment effect (pseudo-progression). Furthermore, there is marked intra-tumoral heterogeneity, such that some areas of a tumor may demonstrate necrotic treatment effect and others frank recurrence. Due to this difficulty reliably differentiating between these two clinical findings, analytic methods using multiple modalities are necessary to further our understanding of this disease process. To this end, we sought to correlate radiographic, histopathologic and molecular features of surgically sampled post-treatment suspected recurrence to identify markers distinguishing tumor growth from treatment effect. We performed Stimulated Raman Histology (SRH) imaging and highly multiplexed RNA-sequencing (PLATE-seq) on 84 MRI-localized biopsies from 39 patients with clinically suspected recurrent HGG. The SRH images were classified as recurrent tumor or gliotic/reactive tissue using a convolutional neural network trained on an independent cohort including a large set of recurrent HGG, and an automated cell-counting algorithm was used to quantify cellularity from the SRH image of each sample. Differential gene expression analysis of the PLATE-seq data was used to identify gene sets that distinguish recurrent tumor from treatment effect, and single sample gene set variation analysis (GSVA) was used to further assess the molecular and cellular composition of each MRI-localized sample. The histopathologic and molecular features of each sample were also correlated with the MRI features of the corresponding biopsy sites, and this data is currently being used to train machine learning models that predict the distribution of recurrent tumor and treatment-induced reactive changes within a patient’s radiographic lesion. These predictive radiomic models will help to guide neurosurgical sampling, and improve our ability to monitor glioma progression and response to therapy.

Although Syrian golden hamsters are widely used as hosts for experimental infection by Schistosoma haematobium, surprisingly little is known about the course of infection and associated intensity (as defined by measures of parasite burden). As such, we sought to define inexpensive, simple, noninvasive, and accurate methods for assessing and predicting the severity of disease in S. haematobium–infected hamsters in order to prevent premature hamster sacrifice and unexpected morbidity and mortality. Through monitoring the weight and behavior of infected hamsters, we determined that the weight-loss patterns of infected hamsters are highly correlated with commonly used measures of the severity of infection (i.e., numbers of eggs passed in the stool, worm burdens, and total egg yields). In contrast, we found no significant correlation between hamster weight-loss patterns and egg yields from liver and intestinal tissues. Our findings suggest that a more complex relationship exists among worm burden, f...