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Article: A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer.

Iyer, Kartik / Ren, Shangsi / Pu, Lucy / Mazur, Summer / Zhao, Xiaoyan / Dhupar, Rajeev / Pu, Jiantao

2023 Volume 15, Issue 13

Abstract: The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between ... ...

Abstract	The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.
Language	English
Publishing date	2023-07-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15133472
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Article: Batch-balanced focal loss: a hybrid solution to class imbalance in deep learning.

Singh, Jatin / Beeche, Cameron / Shi, Zhiyi / Beale, Oliver / Rosin, Boris / Leader, Joseph / Pu, Jiantao

Journal of medical imaging (Bellingham, Wash.)

2023 Volume 10, Issue 5, Page(s) 51809

Abstract: Purpose: To validate the effectiveness of an approach called batch-balanced focal loss (BBFL) in enhancing convolutional neural network (CNN) classification performance on imbalanced datasets.: Materials and methods: BBFL combines two strategies to ... ...

Abstract	Purpose: To validate the effectiveness of an approach called batch-balanced focal loss (BBFL) in enhancing convolutional neural network (CNN) classification performance on imbalanced datasets. Materials and methods: BBFL combines two strategies to tackle class imbalance: (1) batch-balancing to equalize model learning of class samples and (2) focal loss to add hard-sample importance to the learning gradient. BBFL was validated on two imbalanced fundus image datasets: a binary retinal nerve fiber layer defect (RNFLD) dataset ( Results: In binary classification of RNFLD, BBFL with InceptionV3 (93.0% accuracy, 84.7% F1, 0.971 AUC) outperformed ROS (92.6% accuracy, 83.7% F1, 0.964 AUC), cost-sensitive learning (92.5% accuracy, 83.8% F1, 0.962 AUC), and thresholding (91.9% accuracy, 83.0% F1, 0.962 AUC) and others. In multiclass classification of glaucoma, BBFL with MobileNetV2 (79.7% accuracy, 69.6% average F1 score) outperformed ROS (76.8% accuracy, 64.7% F1), cost-sensitive learning (78.3% accuracy, 67.8.8% F1), and random undersampling (76.5% accuracy, 66.5% F1). Conclusion: The BBFL-based learning method can improve the performance of a CNN model in both binary and multiclass disease classification when the data are imbalanced.
Language	English
Publishing date	2023-06-23
Publishing country	United States
Document type	Journal Article
ISSN	2329-4302
ISSN	2329-4302
DOI	10.1117/1.JMI.10.5.051809
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Article: Computational risk modeling of underground coal mines based on NIOSH employment demographics.

Beeche, Cameron A / Garcia, Maria Acevedo / Leng, Shuguang / Roghanchi, Pedram / Pu, Jiantao

Safety science

2023 Volume 164

Abstract: Objective: To investigate the feasibility of predicting the risk of underground coal mine operations using data from the National Institute for Occupational Safety and Health (NIOSH).: Methods: A total of 22,068 data entries from 3,982 unique ... ...

Abstract	Objective: To investigate the feasibility of predicting the risk of underground coal mine operations using data from the National Institute for Occupational Safety and Health (NIOSH). Methods: A total of 22,068 data entries from 3,982 unique underground coal mines from 1990 to 2020 were extracted from the NIOSH mine employment database. We defined the risk index of a mine as the ratio between the number of injuries and the size of the mine. Several machine learning models were used to predict the risk of a mine based on its employment demographics (i.e., number of underground employees, number of surface employees, and coal production). Based on these models, a mine was classified into a "low-risk" or "high-risk" category and assigned with a fuzzy risk index. Risk probabilities were then computed to generate risk profiles and identify mines with potential hazards. Results: NIOSH mine demographic features yielded a prediction performance with an AUC of 0.724 (95% CI 0.717-0.731) based on the last 31-years' mine data and an AUC of 0.738 (95% CI: 0.726, 0.749) on the last 16-years' mine data. Fuzzy risk score shows that risk is greatest in mines with an average of 621 underground employees and a production of 4,210,150 tons. The ratio of tons/employee maximizes the risk at 16,342.18 tons/employee. Conclusion: It is possible to predict the risk of underground coal mines based on their employee demographics and optimizing the allocation and distribution of employees in coal mines can help minimize the risk of accidents and injuries.
Language	English
Publishing date	2023-04-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1074634-1
ISSN	1879-1042 ; 0925-7535
ISSN (online)	1879-1042
ISSN	0925-7535
DOI	10.1016/j.ssci.2023.106170
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Article ; Online: CT-derived body composition associated with lung cancer recurrence after surgery.

Gezer, Naciye S / Bandos, Andriy I / Beeche, Cameron A / Leader, Joseph K / Dhupar, Rajeev / Pu, Jiantao

Lung cancer (Amsterdam, Netherlands)

2023 Volume 179, Page(s) 107189

Abstract: Objectives: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence.: Methods: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had ... ...

Abstract	Objectives: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. Methods: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. Results: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years. Conclusions: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.
MeSH term(s)	Humans ; Lung Neoplasms/pathology ; Retrospective Studies ; Positron Emission Tomography Computed Tomography ; Neoplasm Recurrence, Local ; Lung/pathology ; Body Composition/physiology ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2023-04-08
Publishing country	Ireland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	632771-0
ISSN	1872-8332 ; 0169-5002
ISSN (online)	1872-8332
ISSN	0169-5002
DOI	10.1016/j.lungcan.2023.107189
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Zs.A 2135: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article: The bell tolls for indeterminant lung nodules: computer-aided nodule assessment and risk yield (CANARY) has the wrong tune.

Wilson, David O / Pu, Jiantao

Journal of thoracic disease

2016 Volume 8, Issue 8, Page(s) E836–7

Language	English
Publishing date	2016-09-09
Publishing country	China
Document type	Comment ; Journal Article
ZDB-ID	2573571-8
ISSN	2077-6624 ; 2072-1439
ISSN (online)	2077-6624
ISSN	2072-1439
DOI	10.21037/jtd.2016.07.85
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Article ; Online: Deep-Masker: A Deep Learning-based Tool to Assess Chord Length from Murine Lung Images.

Pu, Jiantao / Leme, Adriana S / de Lima E Silva, Camilla / Beeche, Cameron / Nyunoya, Toru / Königshoff, Melanie / Chandra, Divay

American journal of respiratory cell and molecular biology

2023 Volume 69, Issue 2, Page(s) 126–134

Abstract: Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of chronic obstructive pulmonary disease (COPD). In assessing chord length, the lumens of nonalveolar structures are eliminated from measurement by various ... ...

Abstract	Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of chronic obstructive pulmonary disease (COPD). In assessing chord length, the lumens of nonalveolar structures are eliminated from measurement by various methods, including manual masking. However, manual masking is resource intensive and can introduce variability and bias. We created a fully automated deep learning-based tool to mask murine lung images and assess chord length to facilitate mechanistic and therapeutic discovery in COPD called Deep-Masker (available at http://47.93.0.75:8110/login). We trained the deep learning algorithm for Deep-Masker using 1,217 images from 137 mice from 12 strains exposed to room air or cigarette smoke for 6 months. We validated this algorithm against manual masking. Deep-Masker demonstrated high accuracy with an average difference in chord length compared with manual masking of -0.3 ± 1.4% (
MeSH term(s)	Animals ; Mice ; Deep Learning ; Lung ; Pulmonary Disease, Chronic Obstructive/diagnostic imaging
Language	English
Publishing date	2023-05-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1025960-0
ISSN	1535-4989 ; 1044-1549
ISSN (online)	1535-4989
ISSN	1044-1549
DOI	10.1165/rcmb.2023-0051MA
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Zs.A 2851: Show issues

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Article ; Online: Pulmonary circulatory system characteristics are associated with future lung cancer risk.

Pu, Jiantao / Bandos, Andriy / Yu, Tong / Wang, Renwei / Yuan, Jian-Min / Herman, James / Wilson, David

Medical physics

2023 Volume 51, Issue 4, Page(s) 2589–2597

Abstract: Background: Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung ... ...

Abstract	Background: Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs. Objective: This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting. Methods: We analyzed the PLuSS cohort of 3635 lung screening patients from 2002 to 2016. Circulatory structures were segmented and quantified from LDCT scans. The time from the baseline CT scan to lung cancer diagnosis, accounting for death, was used to evaluate the prognostic ability (i.e., hazard ratio (HR)) of these structures independently and with demographic factors. Five-fold cross-validation was used to evaluate prognostic scores. Results: Intrapulmonary vein volume had the strongest association with future lung cancer (HR = 0.63, p < 0.001). The joint model of intrapulmonary vein volume, age, smoking status, and clinical emphysema provided the strongest prognostic ability (HR = 2.20, AUC = 0.74). The addition of circulatory structures improved risk stratification, identifying the top 10% with 28% risk of lung cancer within 15 years. Conclusion: PCS characteristics, particularly intrapulmonary vein volume, are important predictors of lung cancer development. These factors significantly improve prognostication based on demographic factors and noncirculatory patient characteristics, particularly in the long term. Approximately 10% of the population can be identified with risk several times greater than average.
MeSH term(s)	Humans ; Lung Neoplasms/diagnostic imaging ; Lung/diagnostic imaging ; Pulmonary Emphysema ; Smoking/epidemiology ; Cardiovascular System ; Mass Screening ; Early Detection of Cancer/methods
Language	English
Publishing date	2023-12-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.16930
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Article: CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

Iyer, Kartik / Beeche, Cameron A / Gezer, Naciye S / Leader, Joseph K / Ren, Shangsi / Dhupar, Rajeev / Pu, Jiantao

Journal of clinical medicine

2023 Volume 12, Issue 6

Abstract: Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and ... ...

Abstract	Background: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. Methods: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. Results: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. Conclusions: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.
Language	English
Publishing date	2023-03-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662592-1
ISSN	2077-0383
ISSN	2077-0383
DOI	10.3390/jcm12062106
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Article ; Online: XRayWizard: Reconstructing 3-D lung surfaces from a single 2-D chest x-ray image via Vision Transformer.

Shi, Zhiyi / Geng, Kaiwen / Zhao, Xiaoyan / Mahmoudi, Farhad / Haas, Christopher J / Leader, Joseph K / Duman, Emrah / Pu, Jiantao

Medical physics

2023 Volume 51, Issue 4, Page(s) 2806–2816

Abstract: Background: Chest x-ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost-effectiveness, and portability. However, as a two-dimensional (2-D) imaging modality, chest x-ray images depict limited ... ...

Abstract	Background: Chest x-ray is widely utilized for the evaluation of pulmonary conditions due to its technical simplicity, cost-effectiveness, and portability. However, as a two-dimensional (2-D) imaging modality, chest x-ray images depict limited anatomical details and are challenging to interpret. Purpose: To validate the feasibility of reconstructing three-dimensional (3-D) lungs from a single 2-D chest x-ray image via Vision Transformer (ViT). Methods: We created a cohort of 2525 paired chest x-ray images (scout images) and computed tomography (CT) acquired on different subjects and we randomly partitioned them as follows: (1) 1800 - training set, (2) 200 - validation set, and (3) 525 - testing set. The 3-D lung volumes segmented from the chest CT scans were used as the ground truth for supervised learning. We developed a novel model termed XRayWizard that employed ViT blocks to encode the 2-D chest x-ray image. The aim is to capture global information and establish long-range relationships, thereby improving the performance of 3-D reconstruction. Additionally, a pooling layer at the end of each transformer block was introduced to extract feature information. To produce smoother and more realistic 3-D models, a set of patch discriminators was incorporated. We also devised a novel method to incorporate subject demographics as an auxiliary input to further improve the accuracy of 3-D lung reconstruction. Dice coefficient and mean volume error were used as performance metrics as the agreement between the computerized results and the ground truth. Results: In the absence of subject demographics, the mean Dice coefficient for the generated 3-D lung volumes achieved a value of 0.738 ± 0.091. When subject demographics were included as an auxiliary input, the mean Dice coefficient significantly improved to 0.769 ± 0.089 (p < 0.001), and the volume prediction error was reduced from 23.5 ± 2.7%. to 15.7 ± 2.9%. Conclusion: Our experiment demonstrated the feasibility of reconstructing 3-D lung volumes from 2-D chest x-ray images, and the inclusion of subject demographics as additional inputs can significantly improve the accuracy of 3-D lung volume reconstruction.
MeSH term(s)	Humans ; X-Rays ; Lung/diagnostic imaging ; Thorax ; Tomography, X-Ray Computed/methods ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-10-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.16781
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Article ; Online: Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.

Ren, Shangsi / Beeche, Cameron A / Iyer, Kartik / Shi, Zhiyi / Auster, Quentin / Hawkins, James M / Leader, Joseph K / Dhupar, Rajeev / Pu, Jiantao

Medical physics

2023 Volume 51, Issue 3, Page(s) 1997–2006

Abstract: Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer.: Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used ... ...

Abstract	Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer. Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R Results: The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R Conclusion: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.
MeSH term(s)	Humans ; Positron Emission Tomography Computed Tomography/methods ; Fluorodeoxyglucose F18 ; Esophageal Neoplasms/diagnostic imaging ; Esophageal Neoplasms/surgery ; Positron-Emission Tomography ; Tomography, X-Ray Computed ; Retrospective Studies
Chemical Substances	Fluorodeoxyglucose F18 (0Z5B2CJX4D)
Language	English
Publishing date	2023-07-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	188780-4
ISSN	2473-4209 ; 0094-2405
ISSN (online)	2473-4209
ISSN	0094-2405
DOI	10.1002/mp.16656
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