HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion

Tewary, Suman; Mukhopadhyay, Sudipta

doi:10.1007/s10278-021-00442-5

Cited by 19 publications

(17 citation statements)

References 31 publications

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“…Overall, the DLF model gave more satisfactory and better results than the individual models. This was the same conclusion reached in a previous study [33] where the DLF model was able to minimize the individual model bias and improve the accuracy of the inverse model. Taken together, the above description suggests that adequacy and diversity are two important principles in the selection of base models in the decision-level fusion process [100].…”

Section: Discussionsupporting

confidence: 89%

“…In order to address this issue, we introduced decision-level fusion (DLF) models in ensemble machine learning. The DLF models fuse multichannel/multiscale information and typically produce more consistent and better prediction performance than individual models, have good noise immunity, can handle high-dimensional data, provide complete and detailed object information, and are simple to implement and fast to train [32,33]. These models are extensively used in the fields of injury detection, artificial intelligence, and image processing [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

et al. 2022

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“…Future research should focus on nding novel methods to distinguish the subtle differences between HER2 0 and HER2 low BC. Some studies have already developed arti cial intelligence-algorithms and advanced techniques of targeted mass spectrometry for this purpose, but the studies in this area are still limited and require validation in larger, independent cohorts [27][28][29][30] . Additionally, continuous training of practicing pathologists seems important, as highlighted by the high degree of variability in our study.…”

Section: Discussionmentioning

confidence: 99%

Interobserver variation in the assessment of HER2 low expression in breast cancer: can we improve by adjusting criteria? An international interobserver study.

Navarro

Bockstal

Nawawi³

et al. 2022

Preprint

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The classification of Human Epidermal Growth Factor Receptor 2 (HER2) expression is optimized to detect HER2-amplified breast cancer (BC). However, novel HER2-targeting agents are also effective for BCs with low levels of HER2. This raises the question whether the current guidelines for HER2-testing are sufficiently reproducible to identify HER2 low BC. The aim of this multicenter international study was to assess the interobserver agreement of HER2 low scoring according to the current American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines. Furthermore, we evaluated whether the agreement improved by redefining immunohistochemistry (IHC) scoring criteria, or by adding fluorescent in situ hybridization (FISH). We performed a two-round study of 105 non-amplified BCs. During the first assessment, sixteen pathologists used the latest version of the ASCO/CAP guidelines. After a consensus meeting, the same pathologists scored the same digital slides using modified IHC scoring criteria based on the 2007 ASCO/CAP guideline, and an extra ‘ultralow’ category was added. Overall, the interobserver agreement was limited (4.7% of cases with 100% agreement) in the first round, but this improved by clustering IHC categories. In the second round, the highest reproducibility was seen when comparing IHC 0 versus the grouped cluster of ultralow/1+/2+ (74.3% of cases with 100% agreement). FISH results were not statistically different between HER2 0 and HER2 low cases, regardless of the IHC criteria used. In conclusion, our study suggests that the modified 2007 ASCO/CAP criteria were more reproducible to distinguish HER2 0 from HER2 low cases as compared to the 2018 ASCO/CAP criteria. However, the reproducibility was still moderate, which was not improved by adding FISH. This could lead to suboptimal selection of patients eligible for novel HER2-targeting agents. There is a need for clearer, more reproducible IHC definitions, training and/or development of more accurate methods to detect HER2 low BC.

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“…Routine pathology data also contained immunohistochemical staining for speci c proteins across keratin 5/6 (CK5/6), cadherin E (E-cadherin), multidrug resistance gene 1 (MDR-1), epithelial growth factor receptor (EGFR), and keratin 19 (CK19). The expression score was based on the positive area of the protein (0 -4) × staining intensity (0 -3) (9,10) . TP53, BRAC1/2, BRAF, ATM and PALB2 were determined by rst-generation sequencing.…”

Section: Introductionmentioning

confidence: 99%

Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target and pathological detection

Yiming

Wubulikasimu

Yusuying

2021

Preprint

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Background This study aimed to collect the factors underlying the metastasis of breast cancer and sentinel lymph node, and to screen and analyze the risk factors of sentinel lymph node metastasis, so as to provide reference and basis for clinical work. Methods A total of 100 patients with breast cancer were enrolled in the study. These patients received treatment in our hospital from May 2017 to May 2020. The general information, the characteristics of the color Doppler echocardiography, molybdenum, conventional pathology and molecular pathology of the patients were collected. The influencing factors of sentinel lymph node metastasis in breast cancer patients were retrospectively analyzed. Results In this study, age, tumor diameter and BI-RADS category, pathology type, expression profiles of CK5/6, EGFR, and CK19, and TP53 and BRAC1/2 mutations were independent risk factors for sentinel lymph node metastasis in breast cancer (P<0.05). The number and locations of tumors, the quadrant of tumors, the regularity of tumor margins, the presence of blood flow signals, the presence of posterior echo attenuation, the presence of calcification, histological grade, molecular typing, and mutations of BRAF, ATM and PALB2 were irrelevant factors (P>0.05). Conclusions In conclusion, age, tumor diameter, Bi-rads category, invasive type, expression of CK5/6, EGFR and CK19, and mutations of TP53 and BRAC1/2 were positively correlated with sentinel lymph node metastasis. They are independent risk factors that should be paid more attention in clinical studies, to strengthen the management and control of sentinel lymph node metastasis of high-risk breast cancer, and support early chemotherapy or targeted therapy.

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HER2 Molecular Marker Scoring Using Transfer Learning and Decision Level Fusion

Cited by 19 publications

References 31 publications

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Interobserver variation in the assessment of HER2 low expression in breast cancer: can we improve by adjusting criteria? An international interobserver study.

Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target and pathological detection

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