Early prognosis of metastasis risk in inflammatory breast cancer by texture analysis of tumour microscopic images

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

Using Diffusion Tensor Imaging to Explore the Changes in the Microstructure of Canine Vocal Fold Scar Tissue

OBJECTIVE

To apply diffusion tensor imaging (DTI) in measurement of the diffusion characteristics of water molecules in vocal fold scar tissue, combined with the analysis of textural characteristics of collagen fibers in the cover layer of the vocal folds to explore the feasibility of DTI in the qualitative and quantitative diagnosis of vocal fold scars and the evaluation of microstructural changes of vocal fold scar tissue.

METHODS

A unilateral injury was created using micro-cup forceps in the left vocal fold of six beagles. The contralateral normal vocal fold was used as a self-control. Five months postinjury, the larynges were excised and placed into a magnetic resonance imaging (MRI) system (9.4T BioSpec MRI, Bruker, German) for scanning and extraction of the diffusion parameters, fractional anisotropy (FA) and tensor trace in the anterior, middle, and posterior portions of the vocal fold cover layer. These parameters were then analyzed for statistical significance between the scarred vocal fold and the normal vocal fold. After MRI scanning, the tissue of the vocal folds was divided into anterior, middle, and posterior parts for sectioning and staining with hematoxylin and eosin, and samples were subsequently digitally scanned for texture analysis. The irregularity parameters, energy, contrast, correlation, and homogeneity, of collagen fibers of the vocal folds and the mean gray value of collagen fibers were calculated by the gray-level co-occurrence matrix (GLCM) texture analysis method. The differences in the mean value of the two sides of the vocal fold were compared. In addition, Pearson correlation analysis was performed between DTI parameters and irregularity parameters.

RESULTS

The FA of the left vocal fold cover layer was significantly lower compared to the self-control group (P = 0.0366), and the tensor trace value on the left vocal fold cover layer was significantly higher compared to the self-control group (P = 0.0353). The FA was significantly higher in the anterior part of the right vocal fold cover layer compared to the middle and posterior parts of the same side (P = 0.0352), and the tensor trace was significantly lower in the anterior part of the right vocal fold cover layer compared to the middle and posterior parts of the same side (P = 0.0298). There were no significant differences in FA and tensor trace between the middle and posterior parts of the vocal fold cover layer. The mean gray value of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0219), the energy of the left vocal fold cover layer was significantly smaller than that of the right vocal fold cover layer (P < 0.0001), the contrast of the left vocal folds cover layer was significantly larger than that of the right vocal fold cover layer (P = 0.0002), the correlation of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0002), and the homogeneity of the left vocal folds cover layer was significantly smaller than the right vocal fold cover layer (P = 0.0003). Pearson correlation analysis yielded values of r = 0.926, P = 0.000 between the FA and mean gray value; r = -0.918, P = 0.000 between FA and energy; r = -0.924, P = 0.000 between the FA and homogeneity, r = -0.949, P = 0.000 between tensor trace and mean gray value; r = 0.893, P = 0.000 between the tensor trace and energy; and r = 0.929, P = 0.000 between the tensor trace and homogeneity.

CONCLUSION

FA and tensor trace can be used as effective parameters to reflect microstructural changes in vocal fold scars. DTI is an objective and quantitative method of analyzing vocal fold scarring, and it noninvasively evaluates the microstructure of vocal fold collagen fibers.

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

Background

This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions.

Methods

This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADC_{all b}, mADC_0–1000), BE (mD, mD^*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance.

Results

RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC_0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04).

Conclusions

The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03117-5.

Predicting the prognosis of hepatocellular carcinoma with the treatment of transcatheter arterial chemoembolization combined with microwave ablation using pretreatment MR imaging texture features

OBJECTIVE

To investigate the prognostic value of baseline magnetic resonance imaging (MRI) texture analysis of hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolization (TACE) and microwave ablation (MWA).

METHODS

MRI was performed on 102 patients with HCC before receiving TACE combined with MWA in this retrospective study. The best 10 texture features were screened as a feature group for each MRI sequence by MaZda software using mutual information coefficient (MI), nonlinear discriminant analysis (NDA) and other methods. The optimal feature group with the lowest misdiagnosis rate was achieved on one MRI sequence between two groups dichotomized by 3-year survival, which was used to optimize the significant texture features with the optimal cutoff values. The Cox proportional hazards model was generated for the significant texture features and clinical variables to determine the independent predictors of overall survival (OS). The predictive performance of the model was further evaluated by the area under the ROC curve (AUC). Kaplan-Meier and log-rank tests were performed for disease-free survival (DFS) and Local recurrence-free survival (LRFS).

RESULTS

The optimal feature group with the lowest misdiagnosis rate of 8.82% was obtained on T2WI using MI combined with NDA feature analysis. For Cox proportional hazards regression models, the independent prognostic factors associated with OS were albumin (P = 0.047), BCLC stage (P = 0.001), Correlat_{(1,- 1)T2} (P = 0.01) and SumEntrp_(3,0)T2 (P = 0.015), and the prediction efficiency of multivariate model is AUC = 0.876, 95%CI = 0.803-0.949. Kaplan-Meier analyses further demonstrated that BCLC (P < 0.001), Correlat_{(1,- 1)T2} (P = 0.023) and SumEntrp_(3,0)T2 (P < 0.001) were associated with DFS, and BCLC (P = 0.007) related to LRFS.

CONCLUSIONS

MR imaging texture features may be used to predict the prognosis of HCC treated with TACE combined with MWA.

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques

BACKGROUND

Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer-assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour.

AIM

To identify OSCC based on morphological and textural features of hand-cropped cell nuclei by traditional machine learning methods.

METHODS

In this study, a structure for semi-automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand-cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k-nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images.

RESULTS

We achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features.

CONCLUSION

It is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.

Predicting the pathological response to chemoradiotherapy of non-mucinous rectal cancer using pretreatment texture features based on intravoxel incoherent motion diffusion-weighted imaging

OBJECTIVES

To investigate the performance of the mean parametric values and texture features based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) on identifying pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHODS

Pretreatment IVIM-DWI was performed on 41 LARC patients receiving nCRT in this prospective study. The values of IVIM-DWI parameters (apparent diffusion coefficient, ADC; pure diffusion coefficient, D; pseudo-diffusion coefficient, D* and perfusion fraction, f), the first-order, and gray-level co-occurrence matrix (GLCM) texture features were compared between the pCR (n = 9) and non-pathological responder (non-pCR, n = 32) groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine the efficiency for identifying pCR.

RESULTS

The values of IVIM-DWI parameters and first-order texture features did not show significant differences between the pCR and non-pCR groups. The pCR group had lower Contrast and DifVarnc values extracted from the ADC, D, and D* maps, respectively, as well as lower Correlat_D value. Higher Correlat_D*, Correlat_f, SumAverg_ADC, and SumAverg_D values were observed in the pCR group. The area under the ROC curve (AUC) values for the individual predictors in univariate analysis ranged from 0.698 to 0.837, with sensitivities from 43.75% to 87.50% and specificities from 66.67 to 100.00%. In multivariate analysis, Correlat_D* (P < 0.001), DifVarnc_ADC (P = 0.024), and DifVarnc_D (P < 0.001) were the independent predictors to pCR, with an AUC of 0.986, a sensitivity of 93.75%, and a specificity of 100.00%.

CONCLUSION

Pretreatment GLCM analysis based on IVIM-DWI may be a potential approach to identify the pathological response of LARC.

Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging

The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.

Comparative aspects of canine and human inflammatory breast cancer

Inflammatory breast cancer (IBC) in humans is the most aggressive form of mammary gland cancer and shares clinical, pathologic, and molecular patterns of disease with canine inflammatory mammary carcinoma (CIMC). Despite the use of multimodal therapeutic approaches, including targeted therapies, the prognosis for IBC/CIMC remains poor. The aim of this review is to critically analyze IBC and CIMC in terms of biology and clinical features. While rodent cancer models have formed the basis of our understanding of cancer biology, the translation of this knowledge into improved outcomes has been limited. However, it is possible that a comparative "one health" approach to research, using a natural canine model of the disease, may help advance our knowledge on the biology of the disease. This will translate into better clinical outcomes for both species. We propose that CIMC has the potential to be a useful model for developing and testing novel therapies for IBC. Further, this strategy could significantly improve and accelerate the design and establishment of new clinical trials to identify novel and improved therapies for this devastating disease in a more predictable way.

Prognostic biomarker value of binary and grayscale breast tumor histopathology images

AIM

Breast cancer prognosis is in the spotlight owing to its potentially major clinical importance in effective therapeutic management. Following our recent prognostic establishment of the fractal features calculated on binary breast tumor histopathology images, this study aimed to accomplish the first optimization of this methodology by direct comparison of monofractal, multifractal and co-occurrence algorithms in analysis of binary versus grayscale image formats.

PATIENTS & METHODS

The study included 93 patients with invasive breast cancer, without systemic treatment and a long median follow-up of 150 months.

RESULTS

Grayscale images provided a better prognostic source in comparison to binary, while monofractal, multifractal and co-occurrence image analysis algorithms exerted a comparable performance.

CONCLUSION

The critical prognostic importance of the grayscale texture is revealed.