62 research outputs found
1544P Pre-treatment CT radiomics predicts survival in chemo-immunotherapy-treated small cell lung cancer
Background: The addition of checkpoint inhibitors to chemotherapy in SCLC patients provides modest benefit, with a median survival of 12 months. Development of non-invasive imaging predictors to identify patients most likely to benefit from chemo-immunotherapy would enable personalized management of SCLC.
Methods: A cohort of 31 extensive-stage SCLC patients treated with atezolizumab, carboplatin, and etoposide from June 2020 to May 2021 were identified and pre-treatment CT scans were curated. The axial slice at the level of the carina (S1) was identified and center-cropped. 304 3D radiomic features from 5 slices surrounding S1 were extracted for analysis. After feature selection, the most discriminative radiomic feature was used to train and evaluate a random forest machine classifier for mortality prediction using leave-one-out cross-validation (LOOCV). A baseline classifier was trained using clinical variables. LOOCV mortality probabilities were recorded for each patient and used to stratify patient risk. Overall survival (OS) analysis was performed using Cox modeling.
Results: Median follow-up was 343 days. Patient data included median age of 67 (46-85), race (24 white, 7 black), 58% female, and liver metastases at diagnosis in 29%. The Haralick difference variance feature had an AUC of 0.77 (c-index: 0.70) compared to the clinical baseline AUC of 0.56 (c-index: 0.64) for mortality and OS. The radiomic classifier identified low (N=12) and high (N=19) risk cohorts with median OS of 519.5 and 194 days, respectively (p=.01). There was no significant difference in OS for low and high risk cohorts identified by clinical features (p=0.47).
Conclusions: Patient survival following chemo-immunotherapy in SCLC can be predicted using computational analysis of pre-treatment images. Our results encourage study of larger patient cohorts to further understand the relationship between imaging signatures and survival in SCLC, potentially leading to improved personalized disease management
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Generative AI has received substantial attention in recent years due to its
ability to synthesize data that closely resembles the original data source.
While Generative Adversarial Networks (GANs) have provided innovative
approaches for histopathological image analysis, they suffer from limitations
such as mode collapse and overfitting in discriminator. Recently, Denoising
Diffusion models have demonstrated promising results in computer vision. These
models exhibit superior stability during training, better distribution
coverage, and produce high-quality diverse images. Additionally, they display a
high degree of resilience to noise and perturbations, making them well-suited
for use in digital pathology, where images commonly contain artifacts and
exhibit significant variations in staining. In this paper, we present a novel
approach, namely ViT-DAE, which integrates vision transformers (ViT) and
diffusion autoencoders for high-quality histopathology image synthesis. This
marks the first time that ViT has been introduced to diffusion autoencoders in
computational pathology, allowing the model to better capture the complex and
intricate details of histopathology images. We demonstrate the effectiveness of
ViT-DAE on three publicly available datasets. Our approach outperforms recent
GAN-based and vanilla DAE methods in generating realistic images.Comment: Submitted to MICCAI 202
Topology-Aware Uncertainty for Image Segmentation
Segmentation of curvilinear structures such as vasculature and road networks
is challenging due to relatively weak signals and complex geometry/topology. To
facilitate and accelerate large scale annotation, one has to adopt
semi-automatic approaches such as proofreading by experts. In this work, we
focus on uncertainty estimation for such tasks, so that highly uncertain, and
thus error-prone structures can be identified for human annotators to verify.
Unlike most existing works, which provide pixel-wise uncertainty maps, we
stipulate it is crucial to estimate uncertainty in the units of topological
structures, e.g., small pieces of connections and branches. To achieve this, we
leverage tools from topological data analysis, specifically discrete Morse
theory (DMT), to first capture the structures, and then reason about their
uncertainties. To model the uncertainty, we (1) propose a joint prediction
model that estimates the uncertainty of a structure while taking the
neighboring structures into consideration (inter-structural uncertainty); (2)
propose a novel Probabilistic DMT to model the inherent uncertainty within each
structure (intra-structural uncertainty) by sampling its representations via a
perturb-and-walk scheme. On various 2D and 3D datasets, our method produces
better structure-wise uncertainty maps compared to existing works.Comment: 19 pages, 13 figures, 5 table
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
In medical vision, different imaging modalities provide complementary
information. However, in practice, not all modalities may be available during
inference or even training. Previous approaches, e.g., knowledge distillation
or image synthesis, often assume the availability of full modalities for all
patients during training; this is unrealistic and impractical due to the
variability in data collection across sites. We propose a novel approach to
learn enhanced modality-agnostic representations by employing a meta-learning
strategy in training, even when only limited full modality samples are
available. Meta-learning enhances partial modality representations to full
modality representations by meta-training on partial modality data and
meta-testing on limited full modality samples. Additionally, we co-supervise
this feature enrichment by introducing an auxiliary adversarial learning
branch. More specifically, a missing modality detector is used as a
discriminator to mimic the full modality setting. Our segmentation framework
significantly outperforms state-of-the-art brain tumor segmentation techniques
in missing modality scenarios.Comment: Accepted in ICCV 202
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
To achieve high-quality results, diffusion models must be trained on large
datasets. This can be notably prohibitive for models in specialized domains,
such as computational pathology. Conditioning on labeled data is known to help
in data-efficient model training. Therefore, histopathology reports, which are
rich in valuable clinical information, are an ideal choice as guidance for a
histopathology generative model. In this paper, we introduce PathLDM, the first
text-conditioned Latent Diffusion Model tailored for generating high-quality
histopathology images. Leveraging the rich contextual information provided by
pathology text reports, our approach fuses image and textual data to enhance
the generation process. By utilizing GPT's capabilities to distill and
summarize complex text reports, we establish an effective conditioning
mechanism. Through strategic conditioning and necessary architectural
enhancements, we achieved a SoTA FID score of 7.64 for text-to-image generation
on the TCGA-BRCA dataset, significantly outperforming the closest
text-conditioned competitor with FID 30.1
Automated Assessment of Critical View of Safety in Laparoscopic Cholecystectomy
Cholecystectomy (gallbladder removal) is one of the most common procedures in
the US, with more than 1.2M procedures annually. Compared with classical open
cholecystectomy, laparoscopic cholecystectomy (LC) is associated with
significantly shorter recovery period, and hence is the preferred method.
However, LC is also associated with an increase in bile duct injuries (BDIs),
resulting in significant morbidity and mortality. The primary cause of BDIs
from LCs is misidentification of the cystic duct with the bile duct. Critical
view of safety (CVS) is the most effective of safety protocols, which is said
to be achieved during the surgery if certain criteria are met. However, due to
suboptimal understanding and implementation of CVS, the BDI rates have remained
stable over the last three decades. In this paper, we develop deep-learning
techniques to automate the assessment of CVS in LCs. An innovative aspect of
our research is on developing specialized learning techniques by incorporating
domain knowledge to compensate for the limited training data available in
practice. In particular, our CVS assessment process involves a fusion of two
segmentation maps followed by an estimation of a certain region of interest
based on anatomical structures close to the gallbladder, and then finally
determination of each of the three CVS criteria via rule-based assessment of
structural information. We achieved a gain of over 11.8% in mIoU on relevant
classes with our two-stream semantic segmentation approach when compared to a
single-model baseline, and 1.84% in mIoU with our proposed Sobel loss function
when compared to a Transformer-based baseline model. For CVS criteria, we
achieved up to 16% improvement and, for the overall CVS assessment, we achieved
5% improvement in balanced accuracy compared to DeepCVS under the same
experiment settings
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