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Xiao X, Wang Z, Kong Y, Lu H. Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images. Front Oncol 2023; 13:1081529. [PMID: 36845699 PMCID: PMC9945212 DOI: 10.3389/fonc.2023.1081529] [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: 11/01/2022] [Accepted: 01/20/2023] [Indexed: 02/11/2023] Open
Abstract
Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.
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Affiliation(s)
- Xiao Xiao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China,Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zuoheng Wang
- Department of Biostatistics, Yale University, New Haven, CT, United States
| | - Yan Kong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China,Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China,*Correspondence: Hui Lu, ; Yan Kong,
| | - Hui Lu
- Shanghai Jiao Tong University (SJTU)-Yale Joint Center for Biostatistics and Data Science, National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China,Center for Biomedical Informatics, Shanghai Children’s Hospital, Shanghai, China,*Correspondence: Hui Lu, ; Yan Kong,
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Cheng J, Mao Y, Hong W, Hu W, Shu P, Huang K, Yu J, Jiang M, Li L, Wang W, Ni D, Li S. Multimodal data analysis reveals that pancreatobiliary-type ampullary adenocarcinoma resembles pancreatic adenocarcinoma and differs from cholangiocarcinoma. J Transl Med 2022; 20:272. [PMID: 35705951 PMCID: PMC9199183 DOI: 10.1186/s12967-022-03473-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/05/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Ampullary adenocarcinoma (AAC) arises from the ampulla of Vater where the pancreatic duct and bile duct join and empty into the duodenum. It can be classified into intestinal and pancreatobiliary types based on histopathology or immunohistochemistry. However, there are no biomarkers for further classification of pancreatobiliary-type AAC which has important implications for its treatment. We aimed to identify the tumor origin of pancreatobiliary-type AAC by systematically analyzing whole-slide images (WSIs), survival data, and genome sequencing data collected from multiple centers. METHODS This study involved three experiments. First, we extracted quantitative and highly interpretable features from the tumor region in WSIs and constructed a histologic classifier to differentiate between pancreatic adenocarcinoma (PAC) and cholangiocarcinoma. The histologic classifier was then applied to patients with pancreatobiliary-type AAC to infer the tumor origin. Secondly, we compared the overall survival of patients with pancreatobiliary-type AAC stratified by the adjuvant chemotherapy regimens designed for PAC or cholangiocarcinoma. Finally, we compared the mutation landscape of pancreatobiliary-type AAC with those of PAC and cholangiocarcinoma. RESULTS The histologic classifier accurately classified PAC and cholangiocarcinoma in both the internal and external validation sets (AUC > 0.99). All pancreatobiliary-type AACs (n = 45) were classified as PAC. The patients with pancreatobiliary-type AAC receiving regimens designed for PAC showed more favorable overall survival than those receiving regimens designed for cholangiocarcinoma in a multivariable Cox regression (hazard ratio = 7.24, 95% confidence interval: 1.28-40.78, P = 0.025). The results of mutation analysis showed that the mutation landscape of AAC was very similar to that of PAC but distinct from that of cholangiocarcinoma. CONCLUSIONS This multi-center study provides compelling evidence that pancreatobiliary-type AAC resembles PAC instead of cholangiocarcinoma in different aspects, which can guide the treatment selection and clinical trials planning for pancreatobiliary-type AAC.
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Affiliation(s)
- Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Yize Mao
- Department of Pancreatobiliary Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Wenhui Hong
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Wanming Hu
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Peng Shu
- Molecular Laboratory, Beilun District People's Hospital, Ningbo, China
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
- Regenstrief Institute, Indianapolis, IN, USA
| | - Jingjing Yu
- Department of Pathology, Ningbo Yinzhou No.2 Hospital, Ningbo, China
| | - Maofen Jiang
- Department of Pathology, Beilun District People's Hospital, Ningbo, China
| | - Liqin Li
- Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou Hospital Affiliated With Zhejiang University, Huzhou, China.
| | - Wei Wang
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
- Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, China.
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
| | - Shengping Li
- Department of Pancreatobiliary Surgery, Sun Yat-Sen University Cancer Center, Guangzhou, China.
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Wang X, Barrera C, Bera K, Viswanathan VS, Azarianpour-Esfahani S, Koyuncu C, Velu P, Feldman MD, Yang M, Fu P, Schalper KA, Mahdi H, Lu C, Velcheti V, Madabhushi A. Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors. SCIENCE ADVANCES 2022; 8:eabn3966. [PMID: 35648850 PMCID: PMC9159577 DOI: 10.1126/sciadv.abn3966] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.
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Affiliation(s)
- Xiangxue Wang
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
| | - Cristian Barrera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vidya Sankar Viswanathan
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Sepideh Azarianpour-Esfahani
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Can Koyuncu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Priya Velu
- Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Michael D. Feldman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Yang
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Kurt A. Schalper
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Haider Mahdi
- Magee-Womens Hospital and Magee-Womens Research Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Cheng Lu
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
- Corresponding author. (X.W.); (A.M.)
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