1
|
Yuan L, Shen Z, Shan Y, Zhu J, Wang Q, Lu Y, Shi H. Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis. Front Oncol 2024; 14:1432212. [PMID: 39040448 PMCID: PMC11260632 DOI: 10.3389/fonc.2024.1432212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024] Open
Abstract
Background Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain. Methods The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools. Results A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field. Conclusion In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.
Collapse
Affiliation(s)
- Lei Yuan
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Zhiming Shen
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Yibo Shan
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Jianwei Zhu
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Qi Wang
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Yi Lu
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Hongcan Shi
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| |
Collapse
|
2
|
Zhang X, Qiu Y, Jiang W, Yang Z, Wang M, Li Q, Liu Y, Yan X, Yang G, Shen J. Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma. Radiol Imaging Cancer 2024; 6:e230165. [PMID: 38874529 PMCID: PMC11287226 DOI: 10.1148/rycan.230165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 04/07/2024] [Accepted: 05/13/2024] [Indexed: 06/15/2024]
Abstract
Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.
Collapse
Affiliation(s)
| | | | - Wei Jiang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Zehong Yang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Mengzhu Wang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Qin Li
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Yeqing Liu
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Xu Yan
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Guang Yang
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| | - Jun Shen
- From the Department of Radiology (X.Z., Y.Q., W.J., Z.Y., J.S.),
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene
Regulation, Medical Research Center (X.Z., Y.Q., W.J., Z.Y., Q.L., Y.L., J.S.),
and Department of Pathology (Q.L., Y.L.), Sun Yat-Sen Memorial Hospital, Sun
Yat-Sen University, No. 107 Yanjiang Road West, Guangzhou 510120,
People’s Republic of China; Department of Radiology, the First
People’s Hospital of Kashi Prefecture, Kashi, People’s Republic of
China (Y.Q.); Department of MR Scientific Marketing, Siemens Healthineers,
Guangzhou, People’s Republic of China (M.W., X.Y.); and Shanghai Key
Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East
China Normal University, Shanghai, People’s Republic of China
(G.Y.)
| |
Collapse
|
3
|
Liao H, Yuan J, Liu C, Zhang J, Yang Y, Liang H, Jiang S, Chen S, Li Y, Liu Y. Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma. Insights Imaging 2023; 14:223. [PMID: 38129708 PMCID: PMC10739634 DOI: 10.1186/s13244-023-01553-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/28/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. RESULTS To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. CONCLUSIONS Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. CRITICAL RELEVANCE STATEMENT The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. KEY POINTS • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.
Collapse
Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China
| | - Chunhua Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Jiao Zhang
- Department of Radiology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yaying Yang
- Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Hongwei Liang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Song Jiang
- Department of Radiology, Chongqing Ping An Medical Imaging Diagnosis Center, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.
| | - Yongmei Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
| |
Collapse
|
4
|
Ganesh SR, Roth CM, Parekkadan B. Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma. Bioengineering (Basel) 2023; 10:1360. [PMID: 38135951 PMCID: PMC10740451 DOI: 10.3390/bioengineering10121360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/22/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.
Collapse
Affiliation(s)
- Siddarth R. Ganesh
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Charles M. Roth
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
| | - Biju Parekkadan
- Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA; (S.R.G.); (C.M.R.)
- Department of Medicine, Rutgers Biomedical Health Sciences, New Brunswick, NJ 08852, USA
| |
Collapse
|
5
|
Pyo JS, Kim NY, Min KW, Kang DW. Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1258. [PMID: 37512068 PMCID: PMC10384099 DOI: 10.3390/medicina59071258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/17/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023]
Abstract
Background and Objectives: The present study aimed to elucidate the distribution and the prognostic implications of tumor-stroma ratio (TSR) in various malignant tumors through a meta-analysis. Materials and Methods: This meta-analysis included 51 eligible studies with information for overall survival (OS) or disease-free survival (DFS), according to TSR. In addition, subgroup analysis was performed based on criteria for high TSR. Results: The estimated rate of high TSR was 0.605 (95% confidence interval (CI) 0.565-0.644) in overall malignant tumors. The rates of high TSR ranged from 0.276 to 0.865. The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827-0.895). The estimated high TSR rates of colorectal, esophageal, and stomach cancers were 0.622, 0.529, and 0.448, respectively. In overall cases, patients with high TSR had better OS and DFS than those with low TSR (hazard ratio (HR) 0.631, 95% CI 0.542-0.734, and HR 0.564, 95% CI 0.0.476-0.669, respectively). Significant correlations with OS were found in the breast, cervical, colorectal, esophagus, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, larynx, lung, and stomach cancers. In endometrial cancers, high TSR was significantly correlated with worse OS and DFS. Conclusions: The rate of high TSR was different in various malignant tumors. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors.
Collapse
Affiliation(s)
- Jung-Soo Pyo
- Department of Pathology, Uijeongbu Eulji University Hospital, Eulji University School of Medicine, Uijeongbu-si 11759, Republic of Korea
| | - Nae Yu Kim
- Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu-si 11759, Republic of Korea
| | - Kyueng-Whan Min
- Department of Pathology, Uijeongbu Eulji University Hospital, Eulji University School of Medicine, Uijeongbu-si 11759, Republic of Korea
| | - Dong-Wook Kang
- Department of Pathology, Chungnam National University Sejong Hospital, 20 Bodeum 7-ro, Sejong 30099, Republic of Korea
- Department of Pathology, Chungnam National University School of Medicine, 266 Munhwa Street, Daejeon 35015, Republic of Korea
| |
Collapse
|
6
|
Firmbach D, Benz M, Kuritcyn P, Bruns V, Lang-Schwarz C, Stuebs FA, Merkel S, Leikauf LS, Braunschweig AL, Oldenburger A, Gloßner L, Abele N, Eck C, Matek C, Hartmann A, Geppert CI. Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment. Cancers (Basel) 2023; 15:2675. [PMID: 37345012 DOI: 10.3390/cancers15102675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 06/23/2023] Open
Abstract
The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.
Collapse
Affiliation(s)
- Daniel Firmbach
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Michaela Benz
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Petr Kuritcyn
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Volker Bruns
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Corinna Lang-Schwarz
- Institute of Pathology, Hospital Bayreuth, Preuschwitzer Str. 101, 95445 Bayreuth, Germany
| | - Frederik A Stuebs
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Obstetrics and Gynaecology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Susanne Merkel
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Surgery, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 12, 91054 Erlangen, Germany
| | - Leah-Sophie Leikauf
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Anna-Lea Braunschweig
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Angelika Oldenburger
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Laura Gloßner
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Niklas Abele
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christine Eck
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| |
Collapse
|
7
|
Terada Y, Isaka M, Kawata T, Mizuno K, Muramatsu K, Katsumata S, Konno H, Nagata T, Mizuno T, Serizawa M, Ono A, Sugino T, Shimizu K, Ohde Y. The efficacy of a machine learning algorithm for assessing tumour components as a prognostic marker of surgically resected stage IA lung adenocarcinoma. Jpn J Clin Oncol 2023; 53:161-167. [PMID: 36461783 DOI: 10.1093/jjco/hyac176] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/26/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The importance of the stromal components in tumour progression has been discussed widely, but their prognostic role in small size tumours with lepidic components is not fully understood. Applying digital tissue image analysis to whole-slide imaging may enhance the accuracy and reproducibility of pathological assessment. This study aimed to evaluate the prognostic value of tumour components of lung adenocarcinoma by measuring the dimensions of the tumour consisting elements separately, using a machine learning algorithm. METHODS Between September 2002 and December 2016, 317 patients with surgically resected, pathological stage IA adenocarcinoma with lepidic components were analysed. We assessed the whole tumour area, including the lepidic components, and measured the epithelium, collagen, elastin areas and alveolar air space. We analysed the prognostic impact of each tumour component. RESULTS The dimensions of the epithelium and collagen areas were independent significant risk factors for recurrence-free survival (hazard ratio, 8.38; 95% confidence interval, 1.14-61.88; P = 0.037, and hazard ratio, 2.58; 95% confidence interval, 1.14-5.83; P = 0.022, respectively). According to the subgroup analysis when combining the epithelium and collagen areas as risk factors, patients with tumours consisting of both large epithelium and collagen areas showed significantly poor prognoses (P = 0.002). CONCLUSIONS We assessed tumour components using a machine learning algorithm to stratify the post-operative prognosis of surgically resected stage IA adenocarcinomas. This method might guide the selection of patients with a high risk of recurrence.
Collapse
Affiliation(s)
- Yukihiro Terada
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan.,Division of Thoracic Surgery, Shinshu University School of Medicine, Nagano, Japan
| | - Mitsuhiro Isaka
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Takuya Kawata
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kiyomichi Mizuno
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Koji Muramatsu
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Shinya Katsumata
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Hayato Konno
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Toshiyuki Nagata
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Tetsuya Mizuno
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| | - Masakuni Serizawa
- Drug Discovery and Development Division, Research Institute, Shizuoka Cancer Center, Shizuoka, Japan
| | - Akira Ono
- Division of Thoracic Oncology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Takashi Sugino
- Division of Pathology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kimihiro Shimizu
- Division of Thoracic Surgery, Shinshu University School of Medicine, Nagano, Japan
| | - Yasuhisa Ohde
- Division of Thoracic Surgery, Shizuoka Cancer Center, Shizuoka, Japan
| |
Collapse
|
8
|
Wu B, Moeckel G. Application of digital pathology and machine learning in the liver, kidney and lung diseases. J Pathol Inform 2023; 14:100184. [PMID: 36714454 PMCID: PMC9874068 DOI: 10.1016/j.jpi.2022.100184] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/28/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology.
Collapse
Affiliation(s)
- Benjamin Wu
- Horace Mann School, Bronx, NY, USA,Corresponding author at: 950 Post Rd., Scarsdale, NY 10583, USA.
| | - Gilbert Moeckel
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| |
Collapse
|
9
|
Motono N, Mizoguchi T, Ishikawa M, Iwai S, Iijima Y, Uramoto H. Invasive area to tumor ratio is a significant prognostic factor for non-small cell lung cancer. Thorac Cancer 2022; 13:2935-2940. [PMID: 36177984 PMCID: PMC9626328 DOI: 10.1111/1759-7714.14616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Although T factor is defined as the size of invasive area rather than total tumor size in the eighth edition of the TNM classification, whether the pathological invasive area to tumor ratio (ITR) is a prognostic factor has not yet been evaluated. METHODS In total, 432 lung adenocarcinoma patients were analyzed, among which 266 patients with pathological stage IA were used to perform a subanalysis. RESULTS Smoking status (odds ratio [OR]: 0.43, p = 0.01), neutrophil-to-lymphocyte ratio (NLR) (OR: 1.97, p = 0.03), maximum standardized uptake value (SUVmax ) (OR: 3.62, p < 0.01), and ITR (OR: 6.76, p < 0.01) were significantly different in univariate analysis. Smoking status (OR: 0.34, p < 0.01), SUVmax (OR: 3.05, p < 0.01), and ITR (OR: 5.44, p < 0.01) were risk factors for recurrence in multivariate analysis. In patients with pathological stage IA disease, smoking status (OR: 0.34, p = 0.03), NLR (OR: 2.30, p = 0.04), SUVmax (OR: 3.63, p < 0.01), pathological invasive area (OR: 4.00, p < 0.01), and ITR (OR: 6.03, p < 0.01) were significantly different in univariate analysis. Smoking status (OR: 0.27, p = 0.02), SUVmax (OR: 3.93, p < 0.01), and ITR (OR: 4.38, p < 0.01) were significant risk factors for recurrence in multivariate analysis. CONCLUSIONS SUVmax and ITR are risk factors for recurrence. These results suggest that SUVmax is important for deciding the indication for limited resection or adjuvant chemotherapy, and ITR is an adaptation criterion for adjuvant chemotherapy for early-stage lung adenocarcinoma patients.
Collapse
Affiliation(s)
- Nozomu Motono
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| | - Takaki Mizoguchi
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| | - Masahito Ishikawa
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| | - Shun Iwai
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| | - Yoshihito Iijima
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| | - Hidetaka Uramoto
- Department of Thoracic SurgeryKanazawa Medical UniversityUchinadaJapan
| |
Collapse
|
10
|
Sandlin CW, Gu S, Xu J, Deshpande C, Feldman MD, Good MC. Epithelial cell size dysregulation in human lung adenocarcinoma. PLoS One 2022; 17:e0274091. [PMID: 36201559 PMCID: PMC9536599 DOI: 10.1371/journal.pone.0274091] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
Human cells tightly control their dimensions, but in some cancers, normal cell size control is lost. In this study we measure cell volumes of epithelial cells from human lung adenocarcinoma progression in situ. By leveraging artificial intelligence (AI), we reconstruct tumor cell shapes in three dimensions (3D) and find airway type 2 cells display up to 10-fold increases in volume. Surprisingly, cell size increase is not caused by altered ploidy, and up to 80% of near-euploid tumor cells show abnormal sizes. Size dysregulation is not explained by cell swelling or senescence because cells maintain cytoplasmic density and proper organelle size scaling, but is correlated with changes in tissue organization and loss of a novel network of processes that appear to connect alveolar type 2 cells. To validate size dysregulation in near-euploid cells, we sorted cells from tumor single-cell suspensions on the basis of size. Our study provides data of unprecedented detail for cell volume dysregulation in a human cancer. Broadly, loss of size control may be a common feature of lung adenocarcinomas in humans and mice that is relevant to disease and identification of these cells provides a useful model for investigating cell size control and consequences of cell size dysregulation.
Collapse
Affiliation(s)
- Clifford W. Sandlin
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail: (CWS); (MCG)
| | - Song Gu
- Nanjing University of Information Science and Technology, Nanjing, China
| | - Jun Xu
- Nanjing University of Information Science and Technology, Nanjing, China
| | - Charuhas Deshpande
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Michael D. Feldman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Matthew C. Good
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail: (CWS); (MCG)
| |
Collapse
|
11
|
Zhang X, Ma H, Zhang L, Li F. Predictive Role of Tumor-Stroma Ratio for Survival of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis. Pathol Oncol Res 2022; 27:1610021. [PMID: 35132307 PMCID: PMC8817052 DOI: 10.3389/pore.2021.1610021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 12/14/2021] [Indexed: 11/13/2022]
Abstract
Background: Role of tumor-stroma ratio (TSR) as a predictor of survival in patients with non-small cell lung cancer (NSCLC) remains not clear. A systematic review and meta-analysis was conducted to summarize current evidence for the role of TSR in NSCLC. Methods: Relevant cohort studies were retrieved via search of Medline, Embase, and Web of Science databases. The data was combined with a random-effect model by incorporating the between-study heterogeneity. Specifically, subgroup and meta-regression analyses were performed to explore the association between TSR and survival in patients with squamous cell carcinoma (SCC) or adenocarcinoma (AC). Results: Nine cohort studies with 2031 patients with NSCLC were eligible for the meta-analysis. Pooled results showed that compared to those stroma-poor tumor, patients with stroma rich NSCLC were associated with worse recurrence-free survival (RFS, hazard ratio [HR] = 1.52, 95% confidence interval [CI]: 1.07 to 2.16, p = 0.02) and overall survival (OS, HR = 1.48, 95% CI: 1.20 to 1.82, p < 0.001). Subgroup analyses showed that stroma-rich tumor may be associated with a worse survival of SCC (HR = 1.89 and 1.47 for PFS and OS), but a possibly favorable survival of AC (HR = 0.28 and 0.69 for PFS and OS). Results of meta-regression analysis also showed that higher proportion of patients with SCC was correlated with higher HRs for RFS (Coefficient = 0.012, p = 0.03) and OS (Coefficient = 0.014, p = 0.02) in the included patients, while higher proportion of patients with AC was correlated with lower HRs for RFS (Coefficient = −0.012, p = 0.03) and OS (Coefficient = −0.013, p = 0.04), respectively. Conclusion: Tumor TSR could be used as a predictor of survival in patients with NSCLC. The relative proportion of patients with SCC/AC in the included NSCLC patients may be an important determinant for the association between TSR and survival in NSCLC. Stroma richness may be a predictor of poor survival in patients with lung SCC, but a predictor of better survival in patients with lung AC.
Collapse
Affiliation(s)
- Xuefeng Zhang
- Department of Respiratory and Critical Care Medicine, Yantai Mountain Hospital, Yantai, China
| | - Hongfu Ma
- Department of Respiratory and Critical Care Medicine, Yantai Mountain Hospital, Yantai, China
| | - Liang Zhang
- Department of Respiratory and Critical Care Medicine, Yantai Mountain Hospital, Yantai, China
| | - Fenghuan Li
- Department of Respiratory and Critical Care Medicine, Yantai Mountain Hospital, Yantai, China
| |
Collapse
|
12
|
Meng Y, Zhang H, Li Q, Liu F, Fang X, Li J, Yu J, Feng X, Zhu M, Li N, Jing G, Wang L, Ma C, Lu J, Bian Y, Shao C. CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma. Front Oncol 2021; 11:707288. [PMID: 34820324 PMCID: PMC8606777 DOI: 10.3389/fonc.2021.707288] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 10/18/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility. Results We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively. Conclusions The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.
Collapse
Affiliation(s)
- Yinghao Meng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.,Department of Radiology, No.971 Hospital of Navy, Qingdao, Shandong, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| |
Collapse
|
13
|
Lajara S, Trejo Bittar HE, Monaco SE, Pantanowitz L. Pulmonary carcinomas arising in association with scar: Cytomorphologic features in histologically confirmed cases. Diagn Cytopathol 2021; 49:753-760. [PMID: 33764698 DOI: 10.1002/dc.24737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 02/18/2021] [Accepted: 03/08/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND Lung carcinoma arising in association with scar tissue is a well-reported but much debated phenomenon. Scar tissue complicates imaging and pathologic tumor measurement for cancer staging. To the best of our knowledge, the cytological findings in lung scar carcinoma (LSC) have not been described in the literature. Therefore, the aim of this study was to characterize the findings in fine-needle aspirations (FNA) from histologically confirmed LSCs. METHODS LSCs were identified on retrospective search. Cases with preoperative FNA material were reviewed, including non-scar cases that were used for comparison. The clinical and histopathology findings were recorded. RESULTS Twenty-seven cases associated with scar tissue had material for review and 35 cases not associated with scar tissue were used for comparison. The proportion of fibrosis in resection specimens ranged from 10% to 80%. Five (19%) FNA cases were hypocellular. There was no statistically significant difference between the scar and non-scar groups in terms of overall cellularity and diagnostic categories (P = .113 and P = .17, respectively). There was correlation between cytology and dominant pattern on histology in 19 (79%) adenocarcinoma cases. Spindle cells and fibrous or fibroelastotic fragments were present in 22 (81%) cases. CONCLUSION This is the first study describing the cytology associated with LSCs. The presence of fibrosis did not adversely impact cellularity, which is likely due to multiple excursions and selective microdissection of tumor cells by the FNA needle. The cytomorphological and histological patterns correlated in most cases. FNA is able to provide a preoperative diagnosis of carcinoma despite the presence of fibrosis.
Collapse
Affiliation(s)
- Sigfred Lajara
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Humberto E Trejo Bittar
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Sara E Monaco
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Liron Pantanowitz
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| |
Collapse
|
14
|
Smit MA, Philipsen MW, Postmus PE, Putter H, Tollenaar RA, Cohen D, Mesker WE. The prognostic value of the tumor-stroma ratio in squamous cell lung cancer, a cohort study. Cancer Treat Res Commun 2020; 25:100247. [PMID: 33249210 DOI: 10.1016/j.ctarc.2020.100247] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES The tumor-stroma ratio (TSR) is based on the relative amount of stroma in the primary tumor and has proven to be an independent prognostic factor in various solid tumors. The prognosis of patients and adjuvant treatment decision making in lung squamous cell carcinomas (SqCC) is based on the TNM classification. Currently, no other prognostic biomarkers are available. In this study we evaluated the prognostic value of the TSR in lung SqCC. MATERIAL AND METHODS Patients undergoing lung surgery because of lung SqCC between 2000 and 2018 at the Leiden University Medical Center were included. The TSR was scored on hematoxylin & eosin stained tissue sections. Based on the amount of tumor-stroma, two groups were defined: ≤50% was classified as a stroma-low tumor and >50% as stroma-high. The prognostic value of the TSR was determined with survival analysis. RESULTS A total of 174 stage I-III patients were included. Of them, 79 (45%) were stroma-low and 95 (55%) stroma-high. Separately analyzed for tumor stages, the TSR showed to be an independent prognostic biomarker in stage II (n = 68) for 5-year overall survival (HR=3.0; 95% CI, 1.1-8.6; p = 0.035) and 5-year disease free survival (DFS) (HR=3.6; 95% CI, 1.3-9.9; p = 0.014). Patients with a stroma-high tumor had a worse 5-year DFS in the whole cohort (HR 1.6; 95% CI, 1.0-2.4; p = 0.048), but no independent prognostic value was found. CONCLUSION In stage II lung SqCC patients, stroma-low tumors have a better prognosis compared to stroma-high tumors. Moreover, adjuvant chemotherapy could be spared for these stroma-low patients.
Collapse
Affiliation(s)
- Marloes A Smit
- Department of Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Mark Wh Philipsen
- Department of Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Pieter E Postmus
- Department of Pulmonology, Leiden University Medical Center, Leiden, the Netherlands
| | - Hein Putter
- Department of Medical Statistics, Leiden University Medical Center, Leiden, the Netherlands
| | - Rob Aem Tollenaar
- Department of Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Danielle Cohen
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
| | - Wilma E Mesker
- Department of Surgery, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands.
| |
Collapse
|
15
|
Wang S, Rong R, Yang DM, Fujimoto J, Yan S, Cai L, Yang L, Luo D, Behrens C, Parra ER, Yao B, Xu L, Wang T, Zhan X, Wistuba II, Minna J, Xie Y, Xiao G. Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer. Cancer Res 2020; 80:2056-2066. [PMID: 31915129 PMCID: PMC7919065 DOI: 10.1158/0008-5472.can-19-1629] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 11/15/2019] [Accepted: 12/27/2019] [Indexed: 01/15/2023]
Abstract
The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P = 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. SIGNIFICANCE: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912.
Collapse
Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Shirley Yan
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ling Cai
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Lin Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Danni Luo
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Carmen Behrens
- Department of Thoracic Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Edwin R Parra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Lin Xu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
- Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas.
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| |
Collapse
|
16
|
Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F, Luo X, Yang Y, Yao B, Lin S, Moran C, Kalhor N, Weissferdt A, Minna J, Xie Y, Wistuba II, Mao Y, Xiao G. ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 2019; 50:103-110. [PMID: 31767541 PMCID: PMC6921240 DOI: 10.1016/j.ebiom.2019.10.033] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 10/16/2019] [Accepted: 10/16/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key "hallmarks of cancer". However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. METHODS In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. FINDINGS The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a "spatial map" of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. INTERPRETATION The analysis pipeline developed in this study could convert the pathology image into a "spatial map" of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
Collapse
Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX
| | - Lin Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Faliu Yi
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Xin Luo
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yikun Yang
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - ShinYi Lin
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cesar Moran
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Neda Kalhor
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Annikka Weissferdt
- Department of Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, Department of Internal Medicine and Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), China
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX.
| |
Collapse
|
17
|
Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019; 11:E1673. [PMID: 31661863 PMCID: PMC6895901 DOI: 10.3390/cancers11111673] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. MATERIALS AND METHODS In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. RESULTS We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. DISCUSSION AND CONCLUSION With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.
Collapse
Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Hongyu Liu
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - John Minna
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX 75390, USA.
- Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Ignacio Ivan Wistuba
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| |
Collapse
|
18
|
Fang L, He Y, Liu Y, Ding H, Tong Y, Hu L, Wang C, Zhang Y, Zheng X, Huang P. Adjustment of Microvessel Area by Stromal Area to Improve Survival Prediction in Non-Small Cell Lung Cancer. J Cancer 2019; 10:3397-3406. [PMID: 31293643 PMCID: PMC6603421 DOI: 10.7150/jca.31231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 04/30/2019] [Indexed: 12/11/2022] Open
Abstract
Background: Sustained tumor growth and metastasis require sufficient blood supply, and microvessel area (MVA) has been reported that is related to prognosis of cancer patients. However, tumor cells may not be nourished enough by blood vessels when the cells are separated from vessels by thick stroma. Therefore we investigated whether stroma-area normalized MVA (SnMVA) is a more important prognostic factor than MVA. Materials and Methods: 100 NSCLC patients who underwent resection between July 2011 and October 2012 were randomly selected. We determined the MVA of the tumor tissues by anti-CD31 immunostaining of microvessels. Stroma-area normalized MVA (SnMVA) was a ratio of MVA to stromal area. Correlation of MVA and SnMVA with overall survival (OS) or progression-free survival (PFS) was assessed using multivariate analysis. Results: Median MVA was 0.0228 (range, 0.00393 to 0.172), and median SnMVA was 0.0441 × 10-6 (range, 0.00393 × 10-6 to 0.259 × 10-6). There was no significant difference in OS between groups of different MVA (HR 0.58, 95%CI 0.28 to 1.19, p = 0.148). In contrast, the risk of death was significantly decreased in high SnMVA group (at or below the median) than in group with low SnMVA (HR 0.47, 95%CI 0.23 to 0.97, p = 0.046). Furthermore, in multivariate analysis, high SnMVA, but not MVA, was an independent prognostic factor after adjusting for age, sex, tumor stage and other factors. OS was significantly associated with SnMVA in six of seven subgroup analysis, but with MVA in only three. Conclusions: Our study showed that the NSCLC patients with high SnMVA had higher OS. And SnMVA is a prognostic factor with greater accuracy than MVA. Since stroma exists widely in a variety of cancer tissues, we infer that SnMVA may also predict the prognostic of other types of cancers.
Collapse
Affiliation(s)
- Luo Fang
- Department of Pharmacy, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Ying He
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Yujia Liu
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Yinghui Tong
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Luying Hu
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Canming Wang
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Yiwen Zhang
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Xiaowei Zheng
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| | - Ping Huang
- Department of Pharmacy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, China
| |
Collapse
|
19
|
Zhang X, Zhang Y, Sun Z, Ma H. Research on correlation between tumor-stroma ratio and prognosis in non-small cell lung cancer. Minerva Med 2019; 110:590-592. [PMID: 30843605 DOI: 10.23736/s0026-4806.19.06001-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Xueqing Zhang
- Department of Respiratory Medicine, Jining No.1 People's Hospital, Jining, China
| | - Yan Zhang
- Department of Respiratory Medicine, Jining No.1 People's Hospital, Jining, China
| | - Zongwen Sun
- Department of Respiratory Medicine, Jining No.1 People's Hospital, Jining, China
| | - Huiping Ma
- Department of Respiratory Medicine, Jining No.1 People's Hospital, Jining, China -
| |
Collapse
|
20
|
Hattori A, Takamochi K, Oh S, Suzuki K. New revisions and current issues in the eighth edition of the TNM classification for non-small cell lung cancer. Jpn J Clin Oncol 2019; 49:3-11. [PMID: 30277521 DOI: 10.1093/jjco/hyy142] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 09/24/2018] [Indexed: 12/25/2022] Open
Abstract
In the eighth edition of the TNM classification of lung cancer, the prognostic impact of tumor size is emphasized as a descriptor of all T categories. Especially in lung cancer where tumor size is 5 cm or less, the 1-cm cutoff point significantly differentiated the survival outcome. In addition, the new staging categories were assigned, namely, Tis (adenocarcinoma in situ) and T1mi (minimally invasive adenocarcinoma). Furthermore, the measurement of a radiological solid component size excluding the ground glass opacity component or pathological invasive size without a lepidic component was proposed for deciding the cT/pT categories for lung adenocarcinoma. The N descriptors were kept the same as in the eventh edition on the whole, however, quantification of nodal disease had a prognostic impact based on the number of nodal stations involved in the eighth edition, i.e. N1a as a single N1 station, N1b as a multiple N1 station, N2a1 as a single N2 station without N1 (skip metastasis), N2a2 as a single N2 station with N1 disease, and N2b as a multiple N2 station. In the M descriptors, subclassification was performed based on the location or numbers of distantly metastatic lesions, i.e. M1a as any intrathoracic metastases, M1b as a single distant metastatic lesion in one organ, and M1c as multiple distant metastases in either a single organ or multiple organs. Survival analysis of the eighth edition of the TNM classification clearly separated the distinct groups, however, unsolved issues still remain that should be discussed and further revised for the forthcoming TNM staging system.
Collapse
Affiliation(s)
- Aritoshi Hattori
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kazuya Takamochi
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Shiaki Oh
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Kenji Suzuki
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| |
Collapse
|