Development and External Validation of Deep-Learning-Based Tumor Grading Models in Soft-Tissue Sarcoma Patients Using MR Imaging

Background: In patients with soft-tissue sarcomas, tumor grading constitutes a decisive factor to determine the best treatment decision. Tumor grading is obtained by pathological work-up after focal biopsies. Deep learning (DL)-based imaging analysis may pose an alternative way to characterize STS tissue. In this work, we sought to non-invasively differentiate tumor grading into low-grade (G1) and high-grade (G2/G3) STS using DL techniques based on MR-imaging. Methods: Contrast-enhanced T1-weighted fat-saturated (T1FSGd) MRI sequences and fat-saturated T2-weighted (T2FS) sequences were collected from two independent retrospective cohorts (training: 148 patients, testing: 158 patients). Tumor grading was determined following the French Federation of Cancer Centers Sarcoma Group in pre-therapeutic biopsies. DL models were developed using transfer learning based on the DenseNet 161 architecture. Results: The T1FSGd and T2FS-based DL models achieved area under the receiver operator characteristic curve (AUC) values of 0.75 and 0.76 on the test cohort, respectively. T1FSGd achieved the best F1-score of all models (0.90). The T2FS-based DL model was able to significantly risk-stratify for overall survival. Attention maps revealed relevant features within the tumor volume and in border regions. Conclusions: MRI-based DL models are capable of predicting tumor grading with good reproducibility in external validation.

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Determining breast cancer biomarker status and associated morphological features using deep learning

Communications Medicine ◽

10.1038/s43856-021-00013-3 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Paul Gamble ◽

Ronnachai Jaroensri ◽

Hongwu Wang ◽

Fraser Tan ◽

Melissa Moran ◽

...

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Characteristic Curve ◽

Growth Factor Receptor ◽

Scoring Systems ◽

Cancer Biomarker ◽

Receiver Operator Characteristic Curve ◽

Her2 Status ◽

Low Grade ◽

Cancer Management

Abstract Background Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly preparation and variable interpretation. Additionally, discordances between histology and expected biomarker findings can prompt repeat testing to address biological, interpretative, or technical reasons for unexpected results. Methods We developed three independent deep learning systems (DLS) to directly predict ER/PR/HER2 status for both focal tissue regions (patches) and slides using hematoxylin-and-eosin-stained (H&E) images as input. Models were trained and evaluated using pathologist annotated slides from three data sources. Areas under the receiver operator characteristic curve (AUCs) were calculated for test sets at both a patch-level (>135 million patches, 181 slides) and slide-level (n = 3274 slides, 1249 cases, 37 sites). Interpretability analyses were performed using Testing with Concept Activation Vectors (TCAV), saliency analysis, and pathologist review of clustered patches. Results The patch-level AUCs are 0.939 (95%CI 0.936–0.941), 0.938 (0.936–0.940), and 0.808 (0.802–0.813) for ER/PR/HER2, respectively. At the slide level, AUCs are 0.86 (95%CI 0.84–0.87), 0.75 (0.73–0.77), and 0.60 (0.56–0.64) for ER/PR/HER2, respectively. Interpretability analyses show known biomarker-histomorphology associations including associations of low-grade and lobular histology with ER/PR positivity, and increased inflammatory infiltrates with triple-negative staining. Conclusions This study presents rapid breast cancer biomarker estimation from routine H&E slides and builds on prior advances by prioritizing interpretability of computationally learned features in the context of existing pathological knowledge.

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Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽

10.3390/diagnostics11061127 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1127

Author(s):

Ji Hyung Nam ◽

Dong Jun Oh ◽

Sumin Lee ◽

Hyun Joo Song ◽

Yun Jeong Lim

Keyword(s):

Deep Learning ◽

Small Bowel ◽

Scoring System ◽

Operating Characteristic ◽

Clinical Evidence ◽

Learning Algorithm ◽

Characteristic Curve ◽

External Validation ◽

Test Results ◽

Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Cancers ◽

10.3390/cancers13081929 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1929

Author(s):

Jan C. Peeken ◽

Jan Neumann ◽

Rebecca Asadpour ◽

Yannik Leonhardt ◽

Joao R. Moreira ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcomas ◽

Treatment Decision ◽

Staging System ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Clinical Staging ◽

Imaging Features ◽

Prognostic Assessment ◽

Selection Operator

Background: In patients with soft-tissue sarcomas of the extremities, the treatment decision is currently regularly based on tumor grading and size. The imaging-based analysis may pose an alternative way to stratify patients’ risk. In this work, we compared the value of MRI-based radiomics with expert-derived semantic imaging features for the prediction of overall survival (OS). Methods: Fat-saturated T2-weighted sequences (T2FS) and contrast-enhanced T1-weighted fat-saturated (T1FSGd) sequences were collected from two independent retrospective cohorts (training: 108 patients; testing: 71 patients). After preprocessing, 105 radiomic features were extracted. Semantic imaging features were determined by three independent radiologists. Three machine learning techniques (elastic net regression (ENR), least absolute shrinkage and selection operator, and random survival forest) were compared to predict OS. Results: ENR models achieved the best predictive performance. Histologies and clinical staging differed significantly between both cohorts. The semantic prognostic model achieved a predictive performance with a C-index of 0.58 within the test set. This was worse compared to a clinical staging system (C-index: 0.61) and the radiomic models (C-indices: T1FSGd: 0.64, T2FS: 0.63). Both radiomic models achieved significant patient stratification. Conclusions: T2FS and T1FSGd-based radiomic models outperformed semantic imaging features for prognostic assessment.

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MR-imaging changes of musculoskeletal soft-tissue sarcomas associated with neoadjuvant chemotherapy and hyperthermia

International Journal of Hyperthermia ◽

10.1080/0265673021000058366 ◽

2003 ◽

Vol 19 (4) ◽

pp. 391-401 ◽

Cited By ~ 12

Author(s):

A. Baur ◽

A. Stäbler ◽

C. M. Wendtner ◽

S. Arbogast ◽

S. A. Rahman ◽

...

Keyword(s):

Soft Tissue ◽

Neoadjuvant Chemotherapy ◽

Mr Imaging ◽

Soft Tissue Sarcomas

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Erratum to: Elevated Controlling Nutritional Status (CONUT) Score is Associated with Poor Long-term Survival in Patients with Low-grade Soft-tissue Sarcomas Treated with Surgical Resection

Clinical Orthopaedics and Related Research ◽

10.1097/corr.0000000000002019 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Yao Liang ◽

Tao Hou ◽

Yi Que ◽

Baiwei Zhao ◽

Wei Xiao ◽

...

Keyword(s):

Soft Tissue ◽

Nutritional Status ◽

Surgical Resection ◽

Soft Tissue Sarcomas ◽

Low Grade ◽

Term Survival ◽

Long Term Survival ◽

Conut Score

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Assessing the Relative Value of CT Perfusion Compared to Non-contrast CT and CT Angiography in Prognosticating Reperfusion-Eligible Acute Ischemic Stroke Patients

Frontiers in Neurology ◽

10.3389/fneur.2021.736768 ◽

2021 ◽

Vol 12 ◽

Author(s):

Andrew Bivard ◽

Christopher Levi ◽

Longting Lin ◽

Xin Cheng ◽

Richard Aviv ◽

...

Keyword(s):

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Ct Angiography ◽

Predictive Accuracy ◽

Ct Perfusion ◽

Imaging Modality ◽

Characteristic Curve ◽

Treatment Decision ◽

Receiver Operator Characteristic Curve ◽

Stroke Patients

In the present study we sought to measure the relative statistical value of various multimodal CT protocols at identifying treatment responsiveness in patients being considered for thrombolysis. We used a prospectively collected cohort of acute ischemic stroke patients being assessed for IV-alteplase, who had CT-perfusion (CTP) and CT-angiography (CTA) before a treatment decision. Linear regression and receiver operator characteristic curve analysis were performed to measure the prognostic value of models incorporating each imaging modality. One thousand five hundred and sixty-two sub-4.5 h ischemic stroke patients were included in this study. A model including clinical variables, alteplase treatment, and NCCT ASPECTS was weak (R2 0.067, P < 0.001, AUC 0.605) at predicting 90 day mRS. A second model, including dynamic CTA variables (collateral grade, occlusion severity) showed better predictive accuracy for patient outcome (R2 0.381, P < 0.001, AUC 0.781). A third model incorporating CTP variables showed very high predictive accuracy (R2 0.488, P < 0.001, AUC 0.899). Combining all three imaging modalities variables also showed good predictive accuracy for outcome but did not improve on the CTP model (R2 0.439, P < 0.001, AUC 0.825). CT perfusion predicts patient outcomes from alteplase therapy more accurately than models incorporating NCCT and/or CT angiography. This data has implications for artificial intelligence or machine learning models.

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Well-Differentiated Liposarcoma of The Larynx: A Case Report and Review of Literature

Journal of Biomedical and Clinical Research ◽

10.1515/jbcr-2016-0013 ◽

2016 ◽

Vol 9 (1) ◽

pp. 85-89

Author(s):

Svetlana A. Mateva ◽

Margarita R. Nikolova ◽

Alexandar V. Valkov ◽

Margarita R. Nikolova

Keyword(s):

Case Report ◽

Soft Tissue ◽

Soft Tissue Sarcomas ◽

World Health ◽

Low Grade ◽

Review Of Literature ◽

Grade Malignancy ◽

Well Differentiated ◽

Health Organization ◽

Histologic Types

Summary Liposarcoma is one of the most common soft tissue sarcomas in adults with a relative incidence amongst other sarcomas ranging from 9.8% to 16%. It usually locates in the limbs and retroperitoneum. Primary liposarcomas of the larynx and hypopharynx are rare, comprising less than 20% of all head and neck liposarcomas. According to World Health Organization, these tumors are divided into four histologic types, and well-differentiated liposarcoma is the most common one. It is a tumor of low-grade malignancy that may recur locally, but does not metastasize. We present a case of laryngopharyngeal well- differentiated liposarcoma in an old patient with two previous removals. We also discuss recently published cases with this unusual location of liposarcoma.

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PET–CT for the diagnosis and treatment of primary musculoskeletal tumors in Chinese patients - experience from 255 patients in a single center

British Journal of Radiology ◽

10.1259/bjr.20210785 ◽

2021 ◽

Author(s):

Aikeremujiang Muheremu ◽

Tianlin Wen ◽

Xiaohui Niu

Keyword(s):

Soft Tissue ◽

Case Series ◽

Soft Tissue Sarcomas ◽

Standardized Uptake Value ◽

Chinese Patients ◽

Histopathological Study ◽

Low Grade ◽

High Grade ◽

Musculoskeletal Tumors ◽

Pet Ct

Objective: The current study was carried out to assess the value of positron emission tomography (PET)/CT on the diagnosis and staging of primary musculoskeletal tumors. Methods: PET–CT test results and histopathological study reports of all the patients with primary musculoskeletal tumors in our department from January 2006 to July 2015 were retrospectively reviewed. Maximum standardized uptake value (SUVmax) in these PET–CT reports were recorded and analyzed respectively for each type of sarcoma. Results: A total of 255 patients were included in the final analysis. Sensitivity of SUVmax based diagnosis was 96.6% for primary malignant osseous sarcomas and 91.2% for soft tissue sarcomas. SUVmax of high-grade osseous sarcomas (average 8.4 ± 5.5) was significantly higher (p < 0.001) than low-grade osseous sarcomas (average 3.9 ± 1.8); based on current case series, SUVmax of high-grade soft tissue sarcomas (7.5 ± 5.1) was not significantly different (p = 0.229) from that of low-grade soft tissue sarcomas (5.3 ± 3.7). Significant decrease of SUVmax value after chemotherapy was associated with favorable prognosis in patients with osteosarcoma. Conclusion: Results of the current study indicate that, the SUVmax based application of PET–CT can be a valuable supplementary method to histopathological tests regarding the diagnosis and staging of primary musculoskeletal sarcomas. Advances in knowledge: SUVmax based application of PET–CT is a highly sensitive method in diagnosis of primary osseous and soft tissue sarcomas in Chinese patients.

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2409. External Validation and Comparison of Clostridioides difficile Severity Scoring Systems

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.2087 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S831-S832

Author(s):

Donald A Perry ◽

Daniel Shirley ◽

Dejan Micic ◽

Rosemary K B Putler ◽

Pratish Patel ◽

...

Keyword(s):

Binary Classification ◽

Characteristic Curve ◽

External Validation ◽

Receiver Operator Characteristic Curve ◽

Adverse Outcomes ◽

Risk Scores ◽

Composite Outcome ◽

Clostridioides Difficile ◽

Severity Scores ◽

Primary Composite Outcome

Abstract Background Annually in the US alone, Clostridioides difficile infection (CDI) afflicts nearly 500,000 patients causing 29,000 deaths. Since early and aggressive interventions could save lives but are not optimally deployed in all patients, numerous studies have published predictive models for adverse outcomes. These models are usually developed at a single institution, and largely are not externally validated. This aim of this study was to validate the predictability for severe CDI with previously published risk scores in a multicenter cohort of patients with CDI. Methods We conducted a retrospective study on four separate inpatient cohorts with CDI from three distinct sites: the Universities of Michigan (2010–2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive test. Structured query and manual chart review abstracted data from the medical record at each site. Published CDI severity scores were assessed and compared with each other and the IDSA guideline definition of severe CDI. Sensitivity, specificity, area under the receiver operator characteristic curve (AuROC), precision-recall curves, and net reclassification index (NRI) were calculated to compare models. Results We included 3,775 patients from the four cohorts (Table 1) and evaluated eight severity scores (Table 2). The IDSA (baseline comparator) model showed poor performance across cohorts(Table 3). Of the binary classification models, including those that were most predictive of the primary composite outcome, Jardin, performed poorly with minimal to no NRI improvement compared with IDSA. The continuous score models, Toro and ATLAS, performed better, but the AuROC varied by site by up to 17% (Table 3). The Gujja model varied the most: from most predictive in the University of Michigan 2010–2012 cohort to having no predictive value in the 2016 cohort (Table 3). Conclusion No published CDI severity score showed stable, acceptable predictive ability across multiple cohorts/institutions. To maximize performance and clinical utility, future efforts should focus on a multicenter-derived and validated scoring system, and/or incorporate novel biomarkers. Disclosures All authors: No reported disclosures.

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