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Cai F, Wen J, He F, Xia Y, Xu W, Zhang Y, Jiang L, Li J. SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1505-1515. [PMID: 38424276 PMCID: PMC11300774 DOI: 10.1007/s10278-024-01042-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/13/2024] [Accepted: 02/05/2024] [Indexed: 03/02/2024]
Abstract
Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72 ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.
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Affiliation(s)
- Fenglin Cai
- Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People's Republic of China
| | - Jiaying Wen
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Fangzhou He
- Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People's Republic of China
| | - Yulong Xia
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Weijun Xu
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Yong Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Li Jiang
- Department of Neurosurgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People's Republic of China.
| | - Jie Li
- Department of Intelligent Technology and Engineering, Chongqing University of Science and Technology, Chongqing, 401331, People's Republic of China.
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Gibbins KJ, Roberts VHJ, Lo JO, Boniface ER, Schabel MC, Silver RM, Frias AE. MRI assessed placental volume and adverse pregnancy outcomes: Secondary analysis of prospective cohort study. Placenta 2024; 154:168-175. [PMID: 39018609 DOI: 10.1016/j.placenta.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 07/10/2024] [Accepted: 07/11/2024] [Indexed: 07/19/2024]
Abstract
INTRODUCTION Our goal was to evaluate the potential utility of magnetic resonance imaging (MRI) placental volume as an assessment of placental insufficiency. METHODS Secondary analysis of a prospective cohort undergoing serial placental MRIs at two academic tertiary care centers. The population included 316 participants undergoing MRI up to three times throughout gestation. MRI was used to calculate placental volume in milliliters (ml). Placental-mediated adverse pregnancy outcome (cAPO) included preeclampsia with severe features, abnormal antenatal surveillance, and perinatal mortality. Serial measurements were grouped as time point 1 (TP1) <22 weeks, TP2 22 0/7-29 6/7 weeks, and TP3 ≥30 weeks. Mixed effects models compared change in placental volume across gestation between cAPO groups. Association between cAPO and placental volume was determined using logistic regression at each TP with discrimination evaluated using area under receiver operator curve (AUC). Placental volume was then added to known clinical predictive variables and evaluated with test characteristics and calibration. RESULTS 59 (18.7 %) of 316 participants developed cAPO. Placental volume growth across gestation was slower in the cAPO group (p < 0.001). Placental volume was lower in the cAPO group at all time points, and alone was moderately predictive of cAPO at TP3 (AUC 0.756). Adding placental volume to clinical variables had moderate discrimination at all time points, with strongest test characteristics at TP3 (AUC 0.792) with sensitivity of 77.5 % and specificity of 75.3 % at a predicted probability cutoff of 15 %. DISCUSSION MRI placental volume warrants further study for assessment of placental insufficiency, particularly later in gestation.
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Affiliation(s)
- Karen J Gibbins
- Department of Obstetrics & Gynecology, Oregon Health & Science University, Portland, Oregon, USA.
| | - Victoria H J Roberts
- Oregon National Primate Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Jamie O Lo
- Department of Obstetrics & Gynecology, Oregon Health & Science University, Oregon National Primate Research Center, Portland, Oregon, USA
| | - Emily R Boniface
- Department of Obstetrics & Gynecology, Oregon Health & Science University, Portland, Oregon, USA
| | - Matthias C Schabel
- Advanced Imaging Resource Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Robert M Silver
- Department of Obstetrics & Gynecology, University of Utah Health, Salt Lake City, Utah, USA
| | - Antonio E Frias
- Department of Obstetrics & Gynecology, Oregon Health & Science University, Portland, Oregon, USA
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Fan M, Wang K, Pan D, Cao X, Li Z, He S, Xie S, You C, Gu Y, Li L. Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study. J Transl Med 2024; 22:637. [PMID: 38978099 PMCID: PMC11232151 DOI: 10.1186/s12967-024-05487-y] [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: 04/09/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.
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Affiliation(s)
- Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Kailang Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Da Pan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Xuan Cao
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Zhihao Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Songlin He
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China
| | - Sangma Xie
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China.
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Xiasha High Education Zone, Hangzhou, 310018, Zhejiang, China.
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Li F, Zhu TW, Lin M, Zhang XT, Zhang YL, Zhou AL, Huang DY. Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning. Acad Radiol 2024; 31:2663-2673. [PMID: 38182442 DOI: 10.1016/j.acra.2023.12.036] [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: 11/16/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
RATIONALE AND OBJECTIVES Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. MATERIALS AND METHODS A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. CONCLUSION Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.
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Affiliation(s)
- Fang Li
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Tong-Wei Zhu
- Department of Ultrasound, Taizhou Hospital of Zhejiang Province, Linhai, Zhejiang, China (T.Z.)
| | - Miao Lin
- Second Department of General Surgery, The People's Hospital of Yuhuan, Yuhuan, Zhejiang, China (M.L.)
| | - Xiao-Ting Zhang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Ya-Li Zhang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - Ai-Li Zhou
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.)
| | - De-Yi Huang
- Department of Ultrasound, The People's Hospital of Yuhuan, No. 18, Changle Rd, Yuhuan 317600, Zhejiang, China (F.L., X.Z., Y.Z., A.Z., D.H.).
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Hu B, Xu Y, Gong H, Tang L, Wang L, Li H. Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1-2 Positive Sentinel Nodes. Acad Radiol 2024; 31:2684-2694. [PMID: 38383259 DOI: 10.1016/j.acra.2024.01.026] [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: 11/09/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND In HR+ /HER2- breast cancer patients with ≤ 3 positive axillary lymph nodes (ALNs), genomic tests can streamline chemotherapy decisions. Current studies, centered on tumor metrics, miss broader patient insights. Automated Breast Volume Scanning (ABVS) provides advanced 3D imaging, and its potential synergy with radiomics for ALN evaluation is untapped. OBJECTIVE This study sought to combine ABVS radiomics and clinical characteristics in a nomogram to predict ≤ 3 positive ALNs in HR+ /HER2- breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs), guiding clinicians in genetic test candidate selection. METHODS We enrolled 511 early-stage breast cancer patients: 362 from A Hospital for training and 149 from B Hospital for validation. Using LASSO logistic regression, primary features were identified. A clinical-radiomics nomogram was developed to predict the likelihood of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs. We assessed the discriminative capability of the nomogram using the ROC curve. The model's calibration was confirmed through a calibration curve, while its fit was evaluated using the Hosmer-Lemeshow (HL) test. To determine the clinical net benefits, we employed the Decision Curve Analysis (DCA). RESULTS In the training group, 81.2% patients had ≤ 3 metastatic ALNs, and 83.2% in the validation group. We developed a clinical-radiomics nomogram by analyzing clinical characteristics and rad-scores. Factors like positive SLNs (OR=0.077), absence of negative SLNs (OR=11.138), lymphovascular invasion (OR=0.248), and rad-score (OR=0.003) significantly correlated with ≤ 3 positive ALNs. The clinical-radiomics nomogram, with an AUC of 0.910 in training and 0.882 in validation, outperformed the rad-score-free clinical nomogram (AUCs of 0.796 and 0.782). Calibration curves and the HL test (P values 0.688 and 0.691) confirmed its robustness. DCA showed the clinical-radiomics nomogram provided superior net benefits in predicting ALN burden across specific threshold probabilities. CONCLUSION We developed a clinical-radiomics nomogram that integrated radiomics from ABVS images and clinical data to predict the presence of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs, aiding oncologists in identifying candidates for genomic tests, bypassing ALND. In the era of precision medicine, combining genomic tests with SLN biopsy refines both surgical and systemic patient treatments.
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Affiliation(s)
- Bin Hu
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China.
| | - Yanjun Xu
- Department of Ultrasonography, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Huiling Gong
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Lihong Wang
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Hongchang Li
- Department of General Surgery, Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
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Petrillo A, Fusco R, Barretta ML, Granata V, Mattace Raso M, Porto A, Sorgente E, Fanizzi A, Massafra R, Lafranceschina M, La Forgia D, Trombadori CML, Belli P, Trecate G, Tenconi C, De Santis MC, Greco L, Ferranti FR, De Soccio V, Vidiri A, Botta F, Dominelli V, Cassano E, Boldrini L. Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome. LA RADIOLOGIA MEDICA 2023; 128:1347-1371. [PMID: 37801198 DOI: 10.1007/s11547-023-01718-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/01/2023] [Indexed: 10/07/2023]
Abstract
OBJECTIVE The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.
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Affiliation(s)
- Antonella Petrillo
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Maria Luisa Barretta
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Vincenza Granata
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Mauro Mattace Raso
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annamaria Porto
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Eugenio Sorgente
- Radiology Division, Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, 80131, Naples, Italy
| | - Annarita Fanizzi
- Direzione Scientifica-IRCCS, Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Raffaella Massafra
- SSD Fisica Sanitaria-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Miria Lafranceschina
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiodiagnostica Senologica-IRCCS Istituto Tumori Giovanni Paolo II-Via Orazio Flacco 65, 70124, Bari, Italy
| | | | - Paolo Belli
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
| | - Giovanna Trecate
- Department of Radiodiagnostic and Magnetic Resonance, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Chiara Tenconi
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Maria Carmen De Santis
- De Santis Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133, Milan, Italy
| | - Laura Greco
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Romana Ferranti
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Valeria De Soccio
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Radiology and Diagnostic Imaging, Istituto di Ricovero E Cura a Carattere Scientifico (IRCCS) Regina Elena National Cancer Institute, Rome, Italy
| | - Francesca Botta
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO Istituto Europeo di Oncologia, 20141, Milan, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168, Rome, Italy
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [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: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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Galluzzo A, Boccioli S, Danti G, De Muzio F, Gabelloni M, Fusco R, Borgheresi A, Granata V, Giovagnoni A, Gandolfo N, Miele V. Radiomics in gastrointestinal stromal tumours: an up-to-date review. Jpn J Radiol 2023; 41:1051-1061. [PMID: 37171755 DOI: 10.1007/s11604-023-01441-y] [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: 03/02/2023] [Accepted: 04/29/2023] [Indexed: 05/13/2023]
Abstract
Gastrointestinal stromal tumours are rare mesenchymal neoplasms originating from the Cajal cells and represent the most common sarcomas in the gastroenteric tract. Symptoms may be absent or non-specific, ranging from fatigue and weight loss to acute abdomen. Nowadays endoscopy, echoendoscopy, contrast-enhanced computed tomography, magnetic resonance imaging and positron emission tomography are the main methods for diagnosis. Because of their rarity, these neoplasms may not be included immediately in the differential diagnosis of a solitary abdominal mass. Radiomics is an emerging technique that can extract medical imaging information, not visible to the human eye, transforming it into quantitative data. The purpose of this review is to demonstrate how radiomics can improve the already known imaging techniques by providing useful tools for the diagnosis, treatment, and prognosis of these tumours.
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Affiliation(s)
- Antonio Galluzzo
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Sofia Boccioli
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, Pisa, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013, Naples, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Vincenza Granata
- Department of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione, Pascale-IRCCS di Napoli", 80131, Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica Delle Marche, Via Conca 71, 60126, Ancona, Italy
- Department of Radiology, University Hospital "Azienda Ospedaliera Universitaria Delle Marche", Via Conca 71, 60126, Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149, Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
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Jiang W, Deng X, Zhu T, Fang J, Li J. ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers. BREAST CANCER (DOVE MEDICAL PRESS) 2023; 15:625-636. [PMID: 37600669 PMCID: PMC10439736 DOI: 10.2147/bctt.s418376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/11/2023] [Indexed: 08/22/2023]
Abstract
Background Neoadjuvant chemotherapy (NAC) plays a significant role in breast cancer (BC) management; however, its efficacy varies among patients. Current evaluation methods may lead to delayed treatment alterations, and traditional imaging modalities often yield inaccurate results. Radiomics, an emerging field in medical imaging, offers potential for improved tumor characterization and personalized medicine. Nevertheless, its application in early and accurately predicting NAC response remains underinvestigated. Objective This study aims to develop an automated breast volume scanner (ABVS)-based radiomics model to facilitate early detection of suboptimal NAC response, ultimately promoting personalized therapeutic approaches for BC patients. Methods This retrospective study involved 248 BC patients receiving NAC. Standard guidelines were followed, and patients were classified as responders or non-responders based on treatment outcomes. ABVS images were obtained before and during NAC, and radiomics features were extracted using the PyRadiomics toolkit. Inter-observer consistency and hierarchical feature selection were assessed. Three machine learning classifiers, logistic regression, support vector machine, and random forest, were trained and validated using a five-fold cross-validation with three repetitions. Model performance was comprehensively evaluated based on discrimination, calibration, and clinical utility. Results Of the 248 BC patients, 157 (63.3%) were responders, and 91 (36.7%) were non-responders. Radiomics feature selection revealed 7 pre-NAC and 6 post-NAC ABVS features, with higher weights for post-NAC features (min >0.05) than pre-NAC (max <0.03). The three post-NAC classifiers demonstrated AUCs of approximately 0.9, indicating excellent discrimination. DCA curves revealed a substantial net benefit when the threshold probability exceeded 40%. Conversely, the three pre-NAC classifiers had AUCs between 0.7 and 0.8, suggesting moderate discrimination and limited clinical utility based on their DCA curves. Conclusion The ABVS-based radiomics model effectively predicted suboptimal NAC responses in BC patients, with early post-NAC classifiers outperforming pre-NAC classifiers in discrimination and clinical utility. It could enhance personalized treatment and improve patient outcomes in BC management.
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Affiliation(s)
- Wei Jiang
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Xiaofei Deng
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Ting Zhu
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Jing Fang
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
| | - Jinyao Li
- Department of Ultrasound, Huazhong University of Science and Technology Union Shenzhen Hospital (Nanshan Hospital), Shenzhen, Guangdong province, People’s Republic of China
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10
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Xie Y, Chen Y, Wang Q, Li B, Shang H, Jing H. Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:1638-1646. [PMID: 37100671 DOI: 10.1016/j.ultrasmedbio.2023.03.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 03/15/2023] [Accepted: 03/23/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVE This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. METHODS Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter. RESULTS ∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone. CONCLUSION The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.
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Affiliation(s)
- Yongwei Xie
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yu Chen
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Qiucheng Wang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Bo Li
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Haitao Shang
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China
| | - Hui Jing
- Department of Ultrasound, Harbin Medical University Cancer Hospital, Harbin, China.
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11
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Bellini D, Milan M, Bordin A, Rizzi R, Rengo M, Vicini S, Onori A, Carbone I, De Falco E. A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087214. [PMID: 37108377 PMCID: PMC10138689 DOI: 10.3390/ijms24087214] [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: 02/02/2023] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.
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Affiliation(s)
- Davide Bellini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marika Milan
- UOC Neurology, Fondazione Ca'Granda, Ospedale Maggiore Policlinico, Via F. Sforza, 28, 20122 Milan, Italy
| | - Antonella Bordin
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Roberto Rizzi
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Marco Rengo
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Simone Vicini
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Alessandro Onori
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Iacopo Carbone
- Department of Radiological Sciences, Oncology and Pathology, I.C.O.T. Hospital, Sapienza University of Rome, Via Franco Faggiana 1668, 04100 Latina, Italy
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
| | - Elena De Falco
- Department of Medical Surgical Sciences and Biotechnologies, Sapienza University of Rome, C.so della Repubblica 79, 04100 Latina, Italy
- Mediterranea Cardiocentro, 80122 Napoli, Italy
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12
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Catalano O, Fusco R, De Muzio F, Simonetti I, Palumbo P, Bruno F, Borgheresi A, Agostini A, Gabelloni M, Varelli C, Barile A, Giovagnoni A, Gandolfo N, Miele V, Granata V. Recent Advances in Ultrasound Breast Imaging: From Industry to Clinical Practice. Diagnostics (Basel) 2023; 13:diagnostics13050980. [PMID: 36900124 PMCID: PMC10000574 DOI: 10.3390/diagnostics13050980] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Breast ultrasound (US) has undergone dramatic technological improvement through recent decades, moving from a low spatial resolution, grayscale-limited technique to a highly performing, multiparametric modality. In this review, we first focus on the spectrum of technical tools that have become commercially available, including new microvasculature imaging modalities, high-frequency transducers, extended field-of-view scanning, elastography, contrast-enhanced US, MicroPure, 3D US, automated US, S-Detect, nomograms, images fusion, and virtual navigation. In the subsequent section, we discuss the broadened current application of US in breast clinical scenarios, distinguishing among primary US, complementary US, and second-look US. Finally, we mention the still ongoing limitations and the challenging aspects of breast US.
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Affiliation(s)
- Orlando Catalano
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
- Correspondence:
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
| | - Pierpaolo Palumbo
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Michela Gabelloni
- Department of Translational Research, Diagnostic and Interventional Radiology, University of Pisa, 56126 Pisa, Italy
| | - Carlo Varelli
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, 67100 L’Aquila, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Vincenza Granata
- Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, 80131 Naples, Italy
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13
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Gabelloni M, Faggioni L, Fusco R, Simonetti I, De Muzio F, Giacobbe G, Borgheresi A, Bruno F, Cozzi D, Grassi F, Scaglione M, Giovagnoni A, Barile A, Miele V, Gandolfo N, Granata V. Radiomics in Lung Metastases: A Systematic Review. J Pers Med 2023; 13:jpm13020225. [PMID: 36836460 PMCID: PMC9967749 DOI: 10.3390/jpm13020225] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Due to the rich vascularization and lymphatic drainage of the pulmonary tissue, lung metastases (LM) are not uncommon in patients with cancer. Radiomics is an active research field aimed at the extraction of quantitative data from diagnostic images, which can serve as useful imaging biomarkers for a more effective, personalized patient care. Our purpose is to illustrate the current applications, strengths and weaknesses of radiomics for lesion characterization, treatment planning and prognostic assessment in patients with LM, based on a systematic review of the literature.
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Affiliation(s)
- Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
- Correspondence: ; Tel.: +39-050-992524
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Giuliana Giacobbe
- General and Emergency Radiology Department, “Antonio Cardarelli” Hospital, 80131 Naples, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Federico Bruno
- Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100 L’Aquila, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
| | - Diletta Cozzi
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Mariano Scaglione
- Department of Surgery, Medicine and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60121 Ancona, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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Panico C, Ferrara F, Woitek R, D’Angelo A, Di Paola V, Bufi E, Conti M, Palma S, Cicero SL, Cimino G, Belli P, Manfredi R. Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting. Cancers (Basel) 2022; 14:cancers14235786. [PMID: 36497265 PMCID: PMC9739275 DOI: 10.3390/cancers14235786] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.
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Affiliation(s)
- Camilla Panico
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Correspondence:
| | - Francesca Ferrara
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Ramona Woitek
- Medical Image Analysis and AI (MIAAI), Danube Private University, 3500 Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Cambridge CB2 0RE, UK
| | - Anna D’Angelo
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Valerio Di Paola
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Enida Bufi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Marco Conti
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Simone Palma
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Stefano Lo Cicero
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Giovanni Cimino
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Riccardo Manfredi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
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15
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Correlation Analysis of Pathological Features and Axillary Lymph Node Metastasis in Patients with Invasive Breast Cancer. J Immunol Res 2022; 2022:7150304. [PMID: 36249424 PMCID: PMC9553448 DOI: 10.1155/2022/7150304] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 06/21/2022] [Accepted: 08/05/2022] [Indexed: 11/17/2022] Open
Abstract
Objective To investigate the risk factors of axillary lymph node metastasis in patients with invasive breast cancer. Methods This study retrospectively included 122 cases of invasive breast cancer patients admitted to the First Medical Center of PLA General Hospital from January 2019 to September 2020. According to postoperative pathological results, axillary lymph node metastasis was divided into axillary lymph node metastasis (ALNM) group (n =40) and non-axillary lymph node metastasis (NALNM) group (n =82). General demographic information was collected and compared between the two groups. Collected pathological results included lymphovascular invasion (LVI) and the expression of estrogen receptor (ER), progestogen receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 detected by immunohistochemistry. Imaging parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) including apparent diffusion coefficient (ADC), early enhanced rate, and time-intensity curve (TIC) were also included into univariate analysis. The variables with differences between the two groups were compared by univariate analysis, and the related factors of axillary lymph node metastasis were analyzed by logistic regression model. Results There was no significant difference in general demographic information between the two groups. No significant differences were found in the positive rates of HER-2, ER, PR, Ki-67, pathological types, and clavicular lymph node metastasis and skin chest wall invasion between the two groups (P > 0.05). The proportion of LVI in ALNM group was significantly higher than that in NALNM group (37.50% vs. 6.10%, P < 0.001). The proportion of breast cancer on the left side in the ALNM group was higher than that in the NALNM group, and the difference was statistically significant (70.00% vs. 47.56%, P = 0.019). There were no significant differences in the imaging parameters obtained by DCE-MRI between the two groups. Binary logistics regression analysis showed that LVI (OR =12.258, 95% CI =3.681-40.812, P < 0.001) and left breast cancer (OR =3.598, 95% CI =1.404-9.219, P = 0.008) were risk factors for axillary lymph node metastasis in patients with invasive breast cancer. Conclusion The formation of vascular tumor thrombi in breast cancer tissue and left breast cancer are risk factors for axillary lymph node metastasis in invasive breast cancer and might be helpful for preoperative detailed assessment of the patient's condition.
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Vicini S, Bortolotto C, Rengo M, Ballerini D, Bellini D, Carbone I, Preda L, Laghi A, Coppola F, Faggioni L. A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med 2022; 127:819-836. [DOI: 10.1007/s11547-022-01512-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/01/2022] [Indexed: 12/24/2022]
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17
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Preoperative localisation of nonpalpable breast lesions using magnetic markers in a tertiary cancer centre. Eur Radiol Exp 2022; 6:28. [PMID: 35790602 PMCID: PMC9256869 DOI: 10.1186/s41747-022-00280-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/19/2022] [Indexed: 12/24/2022] Open
Abstract
Background We retrospectively evaluated safety and performance of magnetic seed localisation of nonpalpable breast lesions. Methods We reviewed records of patients with nonpalpable breast lesions preoperative localised by placing magnetic Magseed® marker between February 2019 and December 2020. During surgery, Sentimag® magnetic probe was used to localise the marker and guide surgery. Safety, lesion identification and excision with tumour with free margins and re-excision rate were assessed. Results A total of 77 Magseed® devices were placed into the breasts of 73 patients, 44 under ultrasound and 33 under stereotactic guidance (4 bilateral). All devices were retrieved as were the target lesions. Magnetic marker placement was successful in all cases without any adverse event. Intraoperative identification and excision of the localised lesion were successful in 77 of 77 of cases (100%). In three cases (all of them calcifications with the seed placed under stereotactic guidance), the seed did not reach the exact target position of the biopsy clip; thus, larger excision was needed, with localisation failure attributed to incorrect clip insertion (n = 1) or to clip dislocation (n = 2). Migration of the marker was negligible in all patients. Complete excision after the initial procedure with at least 1-mm disease-free margins was obtained in 74 out of 77 (96.1%) lesions. The re-excision rate was 3 out of 77 (4%). Conclusions Magnetic marker localisation for nonpalpable breast lesions was safe, reliable, and effective in terms of lesion identification, excision with tumour-free margins and re-excision rate.
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Wang H, Yang X, Ma S, Zhu K, Guo S. An Optimized Radiomics Model Based on Automated Breast Volume Scan Images to Identify Breast Lesions: Comparison of Machine Learning Methods: Comparison of Machine Learning Methods. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1643-1655. [PMID: 34609750 DOI: 10.1002/jum.15845] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 08/17/2021] [Accepted: 09/05/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES To develop and test an optimized radiomics model based on multi-planar automated breast volume scan (ABVS) images to identify malignant and benign breast lesions. METHODS Patients (n = 200) with breast lesions who underwent ABVS examinations were included. For each patient, 208 radiomics features were extracted from the ABVS images, including axial plane and coronal plane. Recursive feature elimination, random forest, and chi-square test were used to select features. A support vector machine, logistic regression, and extreme gradient boosting were utilized as classifiers to differentiate malignant and benign breast lesions. The area under the curve, sensitivity, specificity, accuracy, and precision was used to evaluate the performance of the radiomics models. Generalization of the radiomics models was verified through 5-fold cross-validation. RESULTS For a single plane or a combination of planes, a combination of recursive feature elimination, and support vector machine yielded the best performance when identifying breast lesions. The machine learning models based on a combination of planes performed better than those based on a single plane. Regarding the axial plane and coronal plane, the machine learning model using a combination of recursive feature elimination and support vector machine yielded the optimal identification performance: average area under the curve (0.857 ± 0.058, 95% confidence interval, 0.763-0.957); the average values of sensitivity, specificity, accuracy, and precision were 87.9, 68.2, 80.7, and 82.9%, respectively. CONCLUSIONS The optimized radiomics model based on ABVS images can provide valuable information for identifying benign and malignant breast lesions preoperatively and guide the accurate clinical treatment. Further external validation is required.
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Affiliation(s)
- Hui Wang
- The First Clinical Medical College, Lanzhou University, Lanzhou City, China
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou City, China
| | - Xinwu Yang
- College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Sumei Ma
- Department of Ultrasound, The First Hospital of Lanzhou University, Lanzhou City, China
| | - Kongqiang Zhu
- College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Shunlin Guo
- The First Clinical Medical College, Lanzhou University, Lanzhou City, China
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou City, China
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19
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Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022; 12:816297. [PMID: 35669440 PMCID: PMC9163342 DOI: 10.3389/fonc.2022.816297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.
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Affiliation(s)
- Xianshu Kong
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Qian Zhang
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xuemei Wu
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Tianning Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jiajun Duan
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shujie Song
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jianyun Nie
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chu Tao
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Mi Tang
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Maohua Wang
- First Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jieya Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
- *Correspondence: Zhen Li, ; Zhenhui Li,
| | - Zhen Li
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
- *Correspondence: Zhen Li, ; Zhenhui Li,
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20
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:jcm11092599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
- Correspondence:
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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Prediction of Breast Cancer Histological Outcome by Radiomics and Artificial Intelligence Analysis in Contrast-Enhanced Mammography. Cancers (Basel) 2022; 14:cancers14092132. [PMID: 35565261 PMCID: PMC9102628 DOI: 10.3390/cancers14092132] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary The assessment of breast lesions through mammographic images is currently challenging, especially in dense breasts. Contrast-enhanced mammography has been shown to overcome the limitations of standard mammography but it greatly depends on the interpretative skills of the physician. The aim of this study was to evaluate the potentialities of statistical and artificial intelligence algorithms as a tool for helping the radiologists in the interpretation of images. The most remarkable results were achieved in discriminating benign from malignant lesions and in the identification of the presence of the hormone receptor. A tool to support the physician’s decision-making process may be designed starting from simple logistic regression and tree-based algorithms. This type of tool may help the radiologist in assessing the investigated breast and in choosing the appropriate follow-up without resorting to histology. Abstract Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
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22
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Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic. J Pers Med 2022; 12:jpm12040624. [PMID: 35455740 PMCID: PMC9024504 DOI: 10.3390/jpm12040624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 12/17/2022] Open
Abstract
Due to the increasing number of COVID-19-infected and vaccinated individuals, radiologists continue to see patients with COVID-19 pneumonitis and recall pneumonitis, which could result in additional workups and false-positive results. Moreover, cancer patients undergoing immunotherapy may show therapy-related pneumonitis during imaging management. This is otherwise known as immune checkpoint inhibitor-related pneumonitis. Following on from this background, radiologists should seek to know their patients’ COVID-19 infection and vaccination history. Knowing the imaging features related to COVID-19 infection and vaccination is critical to avoiding misleading results and alarmism in patients and clinicians.
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23
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Diagnostic Value of Dynamic Enhanced Magnetic Resonance Imaging Combined with Serum CA15-3, CYFRA21-1, and TFF1 for Breast Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7984591. [PMID: 35392152 PMCID: PMC8983227 DOI: 10.1155/2022/7984591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 11/24/2022]
Abstract
Objective To explore the diagnostic value of dynamic enhanced magnetic resonance imaging (MRI) combined with serum CA15-3, CYFRA21-1, and TFF1 for breast cancer. Methods By means of a retrospective study, 60 breast cancer patients treated in our hospital from January 2018 to December 2020 were selected as the breast cancer group, 60 patients with benign breast lesions were selected as the benign group, and 60 healthy individuals who received physical examination in our hospital in the same period were selected as the control group. All study subjects received dynamic enhanced MRI scan and serological tests, their serum CA15-3 and CYFRA21-1 levels were measured with the electrochemiluminescence instrument and original auxiliary reagent, and the TFF1 level was measured with enzyme-linked immunosorbent assay (ELISA). The MRI performance variation in breast lesion patients was analyzed, the serum CA15-3, CYFRA21-1, and TFF1 levels of study subjects were compared among the three groups, and the efficacy of single diagnosis by dynamic enhanced MRI, CA15-3, CYFRA21-1, or TFF1 as well as combined diagnosis was explored by ROC curves. Results Dynamic enhanced MRI showed that malignant lesion had obscure boundary, irregular margin, and heterogeneity after enhancement, and the time-signal intensity curve presented fast-in fast-out; the benign lesion had a clear boundary and smooth margin, 25 cases showed homogeneity after enhancement, and the time-signal intensity curve presented slow-in slow-out; the CA15-3, CYFRA21-1, and TFF1 levels were significantly different among the breast cancer group, benign group, and control group (33.81 ± 12.46 vs 19.02 ± 6.47 vs 9.55 ± 2.64, 4.08 ± 1.41 vs 1.96 ± 1.19 vs 0.99 ± 0.21, 1.39 ± 0.54 vs 1.04 ± 0.26 vs 0.89 ± 0.12, P < 0.05); 57 breast cancer patients were diagnosed by a combined examination, with a sensitivity of 95.0%, specificity of 83.3%, positive predictive value of 74.0%, negative predictive value of 97.1%, accuracy rate of 87.2%, and AUC (95%CI) = 0.892 (0.840–0.943), indicating a significantly higher diagnostic value of the combined examination than the single examination by CA15-3, CYFRA21-1, TFF1, or MRI. Conclusion Combining dynamic enhanced MRI with serum CA15-3, CYFRA21-1, and TFF1 has good efficacy in diagnosing breast cancer, which can be applied in clinical diagnosis of breast cancer.
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24
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Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022; 40:919-929. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. METHODS This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed. RESULTS Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting. CONCLUSIONS Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
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Affiliation(s)
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy.
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Alessandra Bruno
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100, L'Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
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25
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Structured reporting of x-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med 2022; 127:471-483. [PMID: 35303247 PMCID: PMC9098566 DOI: 10.1007/s11547-022-01478-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/25/2022] [Indexed: 11/05/2022]
Abstract
Background Radiology is an essential tool in the management of a patient. The aim of this manuscript was to build structured report (SR) Mammography based in Breast Cancer. Methods A working team of 16 experts (group A) was composed to create a SR for Mammography Breast Cancer. A further working group of 4 experts (group B), blinded to the activities of the group A, was composed to assess the quality and clinical usefulness of the SR final draft. Modified Delphi process was used to assess level of agreement for all report sections. Cronbach’s alpha (Cα) correlation coefficient was used to assess internal consistency and to measure quality analysis according to the average inter-item correlation.
Results The final SR version was built by including n = 2 items in Personal Data, n = 4 items in Setting, n = 2 items in Comparison with previous breast examination, n = 19 items in Anamnesis and clinical context; n = 10 items in Technique; n = 1 item in Radiation dose; n = 5 items Parenchymal pattern; n = 28 items in Description of the finding; n = 12 items in Diagnostic categories and Report and n = 1 item in Conclusions. The overall mean score of the experts and the sum of score for structured report were 4.9 and 807 in the second round. The Cronbach’s alpha (Cα) correlation coefficient was 0.82 in the second round. About the quality evaluation, the overall mean score of the experts was 3.3. The Cronbach’s alpha (Cα) correlation coefficient was 0.90.
Conclusions Structured reporting improves the quality, clarity and reproducibility of reports across departments, cities, countries and internationally and will assist patient management and improve breast health care and facilitate research.
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Granata V, Fusco R, Vallone P, Setola SV, Picone C, Grassi F, Patrone R, Belli A, Izzo F, Petrillo A. Not only lymphadenopathy: case of chest lymphangitis assessed with MRI after COVID 19 vaccine. Infect Agent Cancer 2022; 17:8. [PMID: 35300727 PMCID: PMC8929244 DOI: 10.1186/s13027-022-00419-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND To date, no paper reports cases of lymphangitis after COVID 19 vaccination. We present a case of lymphangitis after vaccination from COVID 19, in a patient with colorectal liver metastases. METHODS We described the case of a 56-year-old woman with history of a surgical resection of colorectal cancer and liver metastases, without any kind of drug therapy for about a month. In addition, a recent administration (2 days ago) of Spikevax (mRNA-1273, Moderna vaccine), as a booster dose, on the right arm was reported. RESULTS The magnetic resonance (MR) examination showed the effects of the previous surgical resection and five new hepatic metastases, located in the VIII, VI, V, IV and II hepatic segments. As an accessory finding the presence of lymphadenopathy in the axillary area and lymphangitis of the right breast and chest were identified. The computed tomography scan performed a week earlier, and re-evaluated in light of the MR data, did not identify the presence of lymphadenopathy in the axillary area and lymphangitis signs. CONCLUSIONS Lymphangitis could occur after COVID 19 vaccine and it is important to know this data to avoid alarmism in patients and clinicians and economic waste linked to the execution of various radiological investigations for the search for a tumour that probably does not exist. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | | | - Paolo Vallone
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Carmine Picone
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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