1
|
Hu C, Qiao X, Huang R, Hu C, Bao J, Wang X. Development and Validation of a Multimodality Model Based on Whole-Slide Imaging and Biparametric MRI for Predicting Postoperative Biochemical Recurrence in Prostate Cancer. Radiol Imaging Cancer 2024; 6:e230143. [PMID: 38758079 PMCID: PMC11148661 DOI: 10.1148/rycan.230143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 02/16/2024] [Accepted: 03/25/2024] [Indexed: 05/18/2024]
Abstract
Purpose To develop and validate a machine learning multimodality model based on preoperative MRI, surgical whole-slide imaging (WSI), and clinical variables for predicting prostate cancer (PCa) biochemical recurrence (BCR) following radical prostatectomy (RP). Materials and Methods In this retrospective study (September 2015 to April 2021), 363 male patients with PCa who underwent RP were divided into training (n = 254; median age, 69 years [IQR, 64-74 years]) and testing (n = 109; median age, 70 years [IQR, 65-75 years]) sets at a ratio of 7:3. The primary end point was biochemical recurrence-free survival. The least absolute shrinkage and selection operator Cox algorithm was applied to select independent clinical variables and construct the clinical signature. The radiomics signature and pathomics signature were constructed using preoperative MRI and surgical WSI data, respectively. A multimodality model was constructed by combining the radiomics signature, pathomics signature, and clinical signature. Using Harrell concordance index (C index), the predictive performance of the multimodality model for BCR was assessed and compared with all single-modality models, including the radiomics signature, pathomics signature, and clinical signature. Results Both radiomics and pathomics signatures achieved good performance for BCR prediction (C index: 0.742 and 0.730, respectively) on the testing cohort. The multimodality model exhibited the best predictive performance, with a C index of 0.860 on the testing set, which was significantly higher than all single-modality models (all P ≤ .01). Conclusion The multimodality model effectively predicted BCR following RP in patients with PCa and may therefore provide an emerging and accurate tool to assist postoperative individualized treatment. Keywords: MR Imaging, Urinary, Pelvis, Comparative Studies Supplemental material is available for this article. © RSNA, 2024.
Collapse
Affiliation(s)
| | | | - Renpeng Huang
- From the Departments of Radiology (Chenhan Hu, X.Q., Chunhong Hu,
J.B., X.W.) and Pathology (R.H.), the First Affiliated Hospital of Soochow
University, 188 Shizi Road, Suzhou 215006, China
| | - Chunhong Hu
- From the Departments of Radiology (Chenhan Hu, X.Q., Chunhong Hu,
J.B., X.W.) and Pathology (R.H.), the First Affiliated Hospital of Soochow
University, 188 Shizi Road, Suzhou 215006, China
| | - Jie Bao
- From the Departments of Radiology (Chenhan Hu, X.Q., Chunhong Hu,
J.B., X.W.) and Pathology (R.H.), the First Affiliated Hospital of Soochow
University, 188 Shizi Road, Suzhou 215006, China
| | - Ximing Wang
- From the Departments of Radiology (Chenhan Hu, X.Q., Chunhong Hu,
J.B., X.W.) and Pathology (R.H.), the First Affiliated Hospital of Soochow
University, 188 Shizi Road, Suzhou 215006, China
| |
Collapse
|
2
|
Tan Y, Feng LJ, Huang YH, Xue JW, Feng ZB, Long LL. Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer. BMC Cancer 2024; 24:368. [PMID: 38519974 PMCID: PMC10960497 DOI: 10.1186/s12885-024-12021-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/18/2024] [Indexed: 03/25/2024] Open
Abstract
OBJECTIVE This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.
Collapse
Affiliation(s)
- Yang Tan
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Li-Juan Feng
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Ying-He Huang
- Department of Pathology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Jia-Wen Xue
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Zhen-Bo Feng
- Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
| | - Li-Ling Long
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China.
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, Guangxi, China.
| |
Collapse
|
3
|
Jiang W, Wang H, Dong X, Zhao Y, Long C, Chen D, Yan B, Cheng J, Lin Z, Zhuo S, Wang H, Yan J. Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study. J Transl Med 2024; 22:103. [PMID: 38273371 PMCID: PMC10811897 DOI: 10.1186/s12967-024-04851-2] [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: 07/30/2023] [Accepted: 01/02/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. METHODS Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. RESULTS By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations. CONCLUSIONS This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.
Collapse
Affiliation(s)
- Wei Jiang
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- School of Science, Jimei University, Xiamen, Fujian, 361021, People's Republic of China
| | - Huaiming Wang
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Xiaoyu Dong
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yandong Zhao
- Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Chenyan Long
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
- Division of Colorectal and Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, 530000, People's Republic of China
| | - Dexin Chen
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Botao Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Jiaxin Cheng
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Zexi Lin
- School of Science, Jimei University, Xiamen, Fujian, 361021, People's Republic of China
| | - Shuangmu Zhuo
- School of Science, Jimei University, Xiamen, Fujian, 361021, People's Republic of China.
| | - Hui Wang
- Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China.
| | - Jun Yan
- Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China.
- Department of Gastrointestinal Surgery, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, Guangdong, 518020, People's Republic of China.
| |
Collapse
|
4
|
Dia AK, Ebrahimpour L, Yolchuyeva S, Tonneau M, Lamaze FC, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Després P, Manem VSK. The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study. Cancers (Basel) 2024; 16:348. [PMID: 38254838 PMCID: PMC10813866 DOI: 10.3390/cancers16020348] [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: 10/17/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.
Collapse
Affiliation(s)
- Abdou Khadir Dia
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
| | - Leyla Ebrahimpour
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Marion Tonneau
- Lille Faculty of Medicine, University of Lille, 59020 Lille, France
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Fabien C. Lamaze
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Michèle Orain
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Francois Coulombe
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Philippe Joubert
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Philippe Després
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (F.C.L.); (M.O.); (P.J.); (P.D.)
- Department of Physics, Laval University, Quebec City, QC G1V 0A6, Canada
| | - Venkata S. K. Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC G1V 0A6, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, QC G1V 0A6, Canada
| |
Collapse
|
5
|
Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
Collapse
Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
| |
Collapse
|
6
|
Peng W, Wan L, Wang S, Zou S, Zhao X, Zhang H. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Front Oncol 2023; 13:1234619. [PMID: 37664046 PMCID: PMC10468971 DOI: 10.3389/fonc.2023.1234619] [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: 06/05/2023] [Accepted: 06/30/2023] [Indexed: 09/05/2023] Open
Abstract
Objective Radiomics based on magnetic resonance imaging (MRI) shows potential for prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC); however, thorough comparison between radiomics and traditional models is deficient. We aimed to construct multiple-time-scale (pretreatment, posttreatment, and combined) radiomic models to predict pathological complete response (pCR) and compare their utility to those of traditional clinical models. Methods In this research, 165 LARC patients undergoing nCRT followed by surgery were enrolled retrospectively, which were divided into training and testing sets in the ratio of 7:3. Morphological features on pre- and posttreatment MRI, coupled with clinical data, were evaluated by univariable and multivariable logistic regression analysis for constructing clinical models. Radiomic parameters were derived from pre- and posttreatment T2- and diffusion-weighted images to develop the radiomic signatures. The clinical-radiomics models were then generated. All the models were developed in the training set and then tested in the testing set, the performance of which was assessed using the area under the receiver operating characteristic curve (AUC). Radiomic models were compared with the clinical models with the DeLong test. Results One hundred and sixty-five patients (median age, 55 years; age interquartile range, 47-62 years; 116 males) were enrolled in the study. The pretreatment maximum tumor length, posttreatment maximum tumor length, and magnetic resonance tumor regression grade were selected as independent predictors for pCR in the clinical models. In the testing set, the pre- and posttreatment and combined clinical models generated AUCs of 0.625, 0.842, and 0.842 for predicting pCR, respectively. The MRI-based radiomic models performed reasonably well in predicting pCR, but neither the pure radiomic signatures (AUCs, 0.734, 0.817, and 0.801 for the pre- and posttreatment and combined radiomic signatures, respectively) nor the clinical-radiomics models (AUCs, 0.734, 0.860, and 0.801 for the pre- and posttreatment and combined clinical-radiomics models, respectively) showed significant added value compared with the clinical models (all P > 0.05). Conclusion The MRI-based radiomic models exhibited no definite added value compared with the clinical models for predicting pCR in LARC. Radiomic models can serve as ancillary tools for tailoring adequate treatment strategies.
Collapse
Affiliation(s)
- Wenjing Peng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lijuan Wan
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sicong Wang
- Department of Pharmaceutical Diagnosis, GE Healthcare, Life Sciences, Beijing, China
| | - Shuangmei Zou
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongmei Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
7
|
Di Costanzo G, Ascione R, Ponsiglione A, Tucci AG, Dell’Aversana S, Iasiello F, Cavaglià E. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:406-421. [PMID: 37455833 PMCID: PMC10344900 DOI: 10.37349/etat.2023.00142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 03/01/2023] [Indexed: 07/18/2023] Open
Abstract
Rectal cancer (RC) is one of the most common tumours worldwide in both males and females, with significant morbidity and mortality rates, and it accounts for approximately one-third of colorectal cancers (CRCs). Magnetic resonance imaging (MRI) has been demonstrated to be accurate in evaluating the tumour location and stage, mucin content, invasion depth, lymph node (LN) metastasis, extramural vascular invasion (EMVI), and involvement of the mesorectal fascia (MRF). However, these features alone remain insufficient to precisely guide treatment decisions. Therefore, new imaging biomarkers are necessary to define tumour characteristics for staging and restaging patients with RC. During the last decades, RC evaluation via MRI-based radiomics and artificial intelligence (AI) tools has been a research hotspot. The aim of this review was to summarise the achievement of MRI-based radiomics and AI for the evaluation of staging, response to therapy, genotyping, prediction of high-risk factors, and prognosis in the field of RC. Moreover, future challenges and limitations of these tools that need to be solved to favour the transition from academic research to the clinical setting will be discussed.
Collapse
Affiliation(s)
- Giuseppe Di Costanzo
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Raffaele Ascione
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, 80131 Naples, Italy
| | - Anna Giacoma Tucci
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Serena Dell’Aversana
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Francesca Iasiello
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| | - Enrico Cavaglià
- Department of Radiology, Santa Maria delle Grazie Hospital, ASL Napoli 2 Nord, 80078 Pozzuoli, Italy
| |
Collapse
|
8
|
Kang C, Sun P, Yang R, Zhang C, Ning W, Liu H. CT radiomics nomogram predicts pathological response after induced chemotherapy and overall survival in patients with advanced laryngeal cancer: A single-center retrospective study. Front Oncol 2023; 13:1094768. [PMID: 37064100 PMCID: PMC10103838 DOI: 10.3389/fonc.2023.1094768] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/13/2023] [Indexed: 04/03/2023] Open
Abstract
PurposeThis study aimed to develop a radiomics nomogram to predict pathological response (PR) after induction chemotherapy (IC) and overall survival (OS) in patients with advanced laryngeal cancer (LC).MethodsThis retrospective study included patients with LC (n = 114) who had undergone contrast computerized tomography (CT); patients were randomly assigned to training (n = 81) and validation cohorts (n = 33). Potential radiomics scores were calculated to establish a model for predicting the PR status using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression analyses were performed to select significant variables for predicting PR status. Kaplan–Meier analysis was performed to assess the risk stratification ability of PR and radiomics score (rad-score) for predicting OS. A prognostic nomogram was developed by integrating radiomics features and clinicopathological characteristics using multivariate Cox regression. All LC patients were stratified as low- and high-risk by the median CT radiomic score, C-index, calibration curve. Additionally, decision curve analysis (DCA) of the nomogram was performed to test model performance and clinical usefulness.ResultsOverall, PR rates were 45.6% (37/81) and 39.3% (13/33) in the training and validation cohorts, respectively. Eight features were optimally selected to build a rad-score model, which was significantly associated with PR and OS. The median OS in the PR group was significantly shorter than that in the non-PR group in both cohorts. Multivariate Cox analysis revealed that volume [hazard ratio, (HR) = 1.43], N stage (HR = 1.46), and rad-score (HR = 2.65) were independent risk factors associated with OS. The above four variables were applied to develop a nomogram for predicting OS, and the DCAs indicated that the predictive performance of the nomogram was better than that of the clinical model.ConclusionFor patients with advanced LC, CT radiomics score was an independent biomarker for estimating PR after IC. Moreover, the nomogram that incorporated radiomics features and clinicopathological factors performed better for individualized OS estimation.
Collapse
Affiliation(s)
- Chunmiao Kang
- Department of Ultrasound, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Pengfeng Sun
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Runqin Yang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Changming Zhang
- Department of Otolaryngology, Xijing Hospital, Air Force Military Medical University, Xi’an, China
| | - Wenfeng Ning
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hongsheng Liu
- Department of Radiology, Xi’an Central Hospital Affiliated to Xi’an Jiaotong University, Xi’an, Shaanxi, China
- *Correspondence: Hongsheng Liu,
| |
Collapse
|
9
|
He W, Li Q, Li X. Changing patterns of neoadjuvant therapy for locally advanced rectal cancer: A narrative review. Crit Rev Oncol Hematol 2023; 181:103885. [PMID: 36464124 DOI: 10.1016/j.critrevonc.2022.103885] [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: 08/10/2022] [Revised: 10/25/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
Standard treatment for patients with locally advanced rectal cancer has been the multidisciplinary approach of neoadjuvant chemoradiotherapy, followed by total mesorectal excision (TME) and postoperative adjuvant chemotherapy. This reduces the local recurrence rate, but the challenge of distant metastasis still persists. The improvement in treatment approach has always been the focus of clinical research and studies have been conducted worldwide in recent years. On one hand, evidence suggests that increasing the intensity of treatment can result in better tumor regression, for example by adding a second drug to the neoadjuvant chemoradiotherapy, or extending the interval between neoadjuvant therapy and surgery, or incorporating chemotherapy and chemoradiotherapy in the neoadjuvant setting. On the other hand, neoadjuvant immunotherapy and selective omission of neoadjuvant radiotherapy may improve the quality of life of patients. In this article, we review the key clinical research progresses in neoadjuvant therapy for locally advanced rectal cancer, hoping to provide some valuable views on the individualized treatment for rectal cancer.
Collapse
Affiliation(s)
- Weijing He
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Qingguo Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Xinxiang Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| |
Collapse
|
10
|
Wong C, Fu Y, Li M, Mu S, Chu X, Fu J, Lin C, Zhang H. MRI-Based Artificial Intelligence in Rectal Cancer. J Magn Reson Imaging 2023; 57:45-56. [PMID: 35993550 DOI: 10.1002/jmri.28381] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 02/03/2023] Open
Abstract
Rectal cancer (RC) accounts for approximately one-third of colorectal cancer (CRC), with death rates increasing in patients younger than 50 years old. Magnetic resonance imaging (MRI) is routinely performed for tumor evaluation. However, the semantic features from images alone remain insufficient to guide treatment decisions. Functional MRIs are useful for revealing microstructural and functional abnormalities and nevertheless have low or modest repeatability and reproducibility. Therefore, during the preoperative evaluation and follow-up treatment of patients with RC, novel noninvasive imaging markers are needed to describe tumor characteristics to guide treatment strategies and achieve individualized diagnosis and treatment. In recent years, the development of artificial intelligence (AI) has created new tools for RC evaluation based on MRI. In this review, we summarize the research progress of AI in the evaluation of staging, prediction of high-risk factors, genotyping, response to therapy, recurrence, metastasis, prognosis, and segmentation with RC. We further discuss the challenges of clinical application, including improvement in imaging, model performance, and the biological meaning of features, which may also be major development directions in the future. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Chinting Wong
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, China
| | - Yu Fu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Shengnan Mu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Xiaotong Chu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Jiahui Fu
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| | - Chenghe Lin
- Department of Nuclear Medicine, The First Hospital of Jilin University, Changchun, China
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Jilin University, Jilin Provincial Key Laboratory of Medical Imaging and Big Data, Changchun, China
| |
Collapse
|