Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PURPOSE

The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA.

MATERIALS AND METHODS

Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models.

RESULTS

The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60).

CONCLUSION

Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.

One label is all you need: Interpretable AI-enhanced histopathology for oncology

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas

OBJECTIVES

To assess the value of coordinatized lesion location analysis (CLLA), in empowering ROI-based imaging diagnosis of gliomas by improving accuracy and generalization performances.

METHODS

In this retrospective study, pre-operative contrasted T1-weighted and T2-weighted MR images were obtained from patients with gliomas from three centers: Jinling Hospital, Tiantan Hospital, and the Cancer Genome Atlas Program. Based on CLLA and ROI-based radiomic analyses, a fusion location-radiomics model was constructed to predict tumor grades, isocitrate dehydrogenase (IDH) status, and overall survival (OS). An inter-site cross-validation strategy was used for assessing the performances of the fusion model on accuracy and generalization with the value of area under the curve (AUC) and delta accuracy (ACC) (ACCtesting-ACCtraining). Comparisons of diagnostic performances were performed between the fusion model and the other two models constructed with location and radiomics analysis using DeLong's test and Wilcoxon signed ranks test.

RESULTS

A total of 679 patients (mean age, 50 years ± 14 [standard deviation]; 388 men) were enrolled. Based on tumor location probabilistic maps, fusion location-radiomics models (averaged AUC values of grade/IDH/OS: 0.756/0.748/0.768) showed the highest accuracy in contrast to radiomics models (0.731/0.686/0.716) and location models (0.706/0.712/0.740). Notably, fusion models ([median Delta ACC: - 0.125, interquartile range: 0.130]) demonstrated improved generalization than that of radiomics model ([- 0.200, 0.195], p = 0.018).

CONCLUSIONS

CLLA could empower ROI-based radiomics diagnosis of gliomas by improving the accuracy and generalization of the models.

CLINICAL RELEVANCE STATEMENT

This study proposed a coordinatized lesion location analysis for glioma diagnosis, which could improve the performances of the conventional ROI-based radiomics model in accuracy and generalization.

KEY POINTS

• Using coordinatized lesion location analysis, we mapped anatomic distribution patterns of gliomas with specific pathological and clinical features and constructed glioma prediction models. • We integrated coordinatized lesion location analysis into ROI-based analysis of radiomics to propose new fusion location-radiomics models. • Fusion location-radiomics models, with the advantages of being less influenced by variabilities, improved accuracy, and generalization performances of ROI-based radiomics models on predicting the diagnosis of gliomas.

Association of sex-specific abdominal adipose tissue with WHO/ISUP grade in clear cell renal cell carcinoma

OBJECTIVES

To explore the association between computed tomography (CT)-measured sex-specific abdominal adipose tissue and the pathological grade of clear cell renal cell carcinoma (ccRCC).

METHODS

This retrospective study comprised 560 patients (394 males and 166 females) with pathologically proven ccRCC (467 low- and 93 high-grade). Abdominal CT images were used to assess the adipose tissue in the subcutaneous, visceral, and intermuscular regions. Subcutaneous fat index (SFI), visceral fat index (VFI), intermuscular fat index (IFI), total fat index (TFI), and relative visceral adipose tissue (rVAT) were calculated. Univariate and multivariate logistic regression analyses were performed according to sex to identify the associations between fat-related parameters and pathological grade.

RESULTS

IFI was significantly higher in high-grade ccRCC patients than in low-grade patients for both men and women. For male patients with high-grade tumors, the SFI, VFI, TFI, and rVAT were significantly lower, but not for female patients. In both univariate and multivariate studies, the IFI continued to be a reliable and independent predictor of high-grade ccRCC, regardless of sex.

CONCLUSIONS

Intermuscular fat index proved to be a valuable biomarker for the pathological grade of ccRCC and could be used as a reliable independent predictor of high-grade ccRCC for both males and females.

CRITICAL RELEVANCE STATEMENT

Sex-specific fat adipose tissue can be used as a new biomarker to provide a new dimension for renal tumor-related research and may provide new perspectives for personalized tumor management decision-making approaches.

KEY POINTS

• There are sex differences in distribution of subcutaneous fat and visceral fat. • The SFI, VFI, TFI, and rVAT were significantly lower in high-grade ccRCC male patients, but not for female patients. • Intermuscular fat index can be used as a reliable independent predictor of high-grade ccRCC for both males and females.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status

OBJECTIVES

The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas.

METHODS

Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (D_int and D_p) and fractions of strictly limited, intermediate, and perfusion-related diffusion (F_very-slow, F_int, and F_p) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed.

RESULTS

High-grade gliomas (HGGs) had significantly higher F_int, F_very-slow, and D_p values but significantly lower F_p and D_int values than low-grade gliomas (LGGs), and F_int and F_p differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). F_int achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, F_int, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, F_int remained the only differentiator. Additionally, F_int and F_p showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively).

CONCLUSIONS

The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses.

KEY POINTS

• The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • F_int was useful in evaluating the grade and genotype of gliomas.

[Primary salivary gland tumors from a pathology perspective : Morphomolecular peculiarities and diagnostic and therapeutic challenges]

Similar to tumors of other organs, salivary gland neoplasms were historically viewed as a single neoplastic entity and mostly treated as such. Accordingly, only the clinical tumor stage, and not the histological subtype, was considered to be of significant prognostic impact. However, over the years, several distinct sub-entities have been characterized based on morphological features, such as adenoid cystic carcinoma, mucoepidermoid carcinoma, acinic cell carcinoma, and salivary duct carcinoma. Most importantly, the nosology of salivary gland carcinomas has undergone a dynamic "splitting" on the basis of morphological, immunophenotypic, and molecular characteristics, so that 21 independent carcinomas are now listed in the current World Health Organization (WHO) classification. Moreover, it has become evident that splitting of these carcinoma subtypes no longer represents a "pathologist's hobby," but carries significant prognostic and therapeutic relevance for optimized cancer surgery and potentially systemic therapy. The current review summarizes the major features of salivary gland tumors, both benign and malignant, and gives an account of their classification systems and genetic profiles.

Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention

Tumor grading and interpretability of laryngeal cancer is a key yet challenging task in the clinical diagnosis, mainly because of the commonly used low-magnification pathological images lack fine cellular structure information and accurate localization, the diagnosis results of pathologists are different from those of attentional convolutional network -based methods, and the gradient-weighted class activation mapping method cannot be optimized to create the best visualization map. To address this problem, we propose an end-to-end depth domain adaptive network (DDANet) with integration gradient CAM and priori experience-guided attention to improve the tumor grading performance and interpretability by introducing the pathologist's a priori experience in high-magnification into the depth model. Specifically, a novel priori experience-guided attention (PE-GA) method is developed to solve the traditional unsupervised attention optimization problem. Besides, a novel integration gradient CAM is proposed to mitigate overfitting, information redundancies and low sparsity of the Grad-CAM graphs generated by the PE-GA method. Furthermore, we establish a set of quantitative evaluation metric systems for model visual interpretation. Extensive experimental results show that compared with the state-of-the-art methods, the average grading accuracy is increased to 88.43% (↑4.04%), the effective interpretable rate is increased to 52.73% (↑11.45%). Additionally, it effectively reduces the difference between CV-based method and pathology in diagnosis results. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and our model outperforms pathologists with different levels of experience.

A comparative study of quantitative metrics in chemical exchange saturation transfer imaging for grading gliomas in adults

PURPOSE

To evaluate the performance of different chemical exchange saturation transfer (CEST) metrics for grading gliomas with semiautomatically defined regions of interest (ROIs).

METHODS

Thirty-eight adult subjects were included, including 23 high-grade gliomas and 15 low-grade gliomas confirmed by histopathology. The B0-corrected CEST z-spectra were first calculated with magnetization transfer ratio asymmetry (MTR_asym) analysis at frequency offsets of 3.5, 3, 2.5, 2, 1.5, and 1 ppm to obtain the fit-free metrics and subsequently fitted with three Lorentzian functions denoting direct water saturation (DS), amide proton transfer (APT), and combined semisolid magnetization transfer and nuclear Overhauser enhancement (MT & NOE) effects to derive the fit-based metrics. Wilcoxon rank-sum test was performed to determine if a statistically significant difference was present in the CEST metrics between low- and high-grade gliomas. Receiver operating characteristic (ROC) curves were used to evaluate the differentiation of CEST metrics between low- and high-grade gliomas. Pearson correlation coefficients were employed to evaluate the correlations of CEST metrics.

RESULTS

For the fit-free metrics, the highest areas under the curve (AUCs) of 0.85, 0.88, and 0.88, corresponding to MTR_asym, MTR_normref (normalization by the reference scan), and MTR_Rex (subtraction of inverse z-spectra), respectively, were obtained at 3 ppm across various frequency offsets. In addition, the AUCs generated from the fit-based metrics (0.88-0.90) were higher than those generated from the fit-free metrics at 3 ppm.

CONCLUSION

The results of this preliminary study indicate that fit-free CEST metrics at 3 ppm are superior to the other frequency offsets for grading human brain gliomas. The fit-based metrics manifested improved differentiation between low- and high-grade gliomas compared to the fit-free CEST metrics.

WHO grading system for invasive pulmonary lung adenocarcinoma reveals distinct molecular signature: An analysis from the cancer genome atlas database

Lung adenocarcinoma grading has gained interest in the past years. Recently a three-tier tumor grading was proposed showing that it is related to patients' prognosis. Nevertheless, the underlying molecular basis of this morphological grading remains partly unknown. The aim of our work is to take advantage of The Cancer Genome Atlas lung adenocarcinoma (TCGA_LUAD) cohort to describe the molecular data associated to tumor grading. We performed a study on publicly available data of the TCGA database first by assessing a tumor grade on downloadable tumor slides. Secondly we analyzed the molecular features of each tumor grade group. Our work was performed on a study group of 449 patients. We show that aneuploidy score was significantly different between grade 2 and grade 3 groups with different chromosomal imbalance (p < 0.001). SCGB1A1 mRNA expression was higher in grade 2 (p = 0.0179) whereas NUP155, CHFR, POLQ and CDC7 have a higher expression in grade 3 (p = 0.0189, 0.0427, 0.0427 and 0.427 respectively). GZMB and KRT80 have a higher methylation of DNA in grade 2 (p = 0.0201 and 0.0359 respectively). MT1G, CLEC12B and NDUFA7 have a higher methylation of DNA in grade 3 (p < 0.001, 0.0246 and 0.0359 respectively). We showed that the number of activated pathways is different between grade 2 and grade 3 patients (p = 0.004). We showed that differentially expressed genes by mRNA analysis and DNA methylation analysis involve several genes implied in chemoresistance. This could suggest that grade 3 lung adenocarcinoma might be more resistant to chemotherapy.

[Tumor budding in colorectal cancer-Information for clinical use and instructions for practical evaluation]

BACKGROUND

Some patients with high-risk colorectal cancer show a worse prognosis within the same UICC stage. Therefore, the identification of additional risk factors is necessary to find the best treatment for these patients.

OBJECTIVE

In which settings can tumor budding help the clinical decision-making process for treatment planning and how should scoring be performed?

MATERIAL AND METHODS

Evaluation of current publications on tumor budding with an emphasis on practical grading and potential problems in the determination of tumor budding.

RESULTS

Tumor budding is a significant risk factor for worse clinical outcome of colorectal cancer and can influence clinical decision-making in pT1 and stage II colorectal cancer. A scoring method was standardized by the ITBCC 2016 and is feasible in everyday practice. Challenges in assessment can be addressed by increasing awareness of potential problem cases.

High-grade tumor classified by new system is a prognostic predictor in resected lung adenocarcinoma

OBJECTIVES

A grading system for pulmonary adenocarcinoma has not been established; hence, the International Association for the Study of Lung Cancer (IASLC) pathology panel developed a new grading system for invasive adenocarcinoma. We aimed to evaluate the prognostic significance of the IASLC grading system for invasive pulmonary adenocarcinoma.

METHODS

We conducted a retrospective analysis of 471 Japanese patients with resected lung adenocarcinoma. Tumors were classified in accordance with the IASLC grading system and 2015 World Health Organization classification. We analyzed recurrence-free probability (RFP) and overall survival (OS) using the log-rank test and compared the two grading systems using the Cox proportional hazards model.

RESULTS

Grade 3 tumors of the IASLC system and high-grade tumors of the 2015 World Health Organization classification were present in 38% and 17% of patients, respectively. The 5-year RFP was lower in patients with IASLC Grade 3 tumors (45%) than in patients with IASLC Grade 1 and 2 tumors (91% and 83%, respectively). The 5-year RFP of patients with IASLC Grade 2 tumors (83%) was higher than of those with 2015 World Health Organization intermediate tumors (69%). On multivariate analysis for recurrence, IASLC Grade 3 was an independent prognostic factor of worse RFP. We showed similar results on analysis for the OS.

CONCLUSIONS

The prognostic significance of IASLC Grade 3 tumors on recurrence-free probability was confirmed through both univariate and multivariate analyses. Thus, the IASLC Grade 3 tumor is an independent factor of poor prognosis in patients with resected lung adenocarcinoma.

Genome-wide DNA methylome analysis identifies methylation signatures associated with survival and drug resistance of ovarian cancers

BACKGROUND

In contrast to stable genetic events, epigenetic changes are highly plastic and play crucial roles in tumor evolution and development. Epithelial ovarian cancer (EOC) is a highly heterogeneous disease that is generally associated with poor prognosis and treatment failure. Profiling epigenome-wide DNA methylation status is therefore essential to better characterize the impact of epigenetic alterations on the heterogeneity of EOC.

METHODS

An epigenome-wide association study was conducted to evaluate global DNA methylation in a retrospective cohort of 80 mixed subtypes of primary ovarian cancers and 30 patients with high-grade serous ovarian carcinoma (HGSOC). Three demethylating agents, azacytidine, decitabine, and thioguanine, were tested their anti-cancer and anti-chemoresistant effects on HGSOC cells.

RESULTS

Global DNA hypermethylation was significantly associated with high-grade tumors, platinum resistance, and poor prognosis. We determined that 9313 differentially methylated probes (DMPs) were enriched in their relative gene regions of 4938 genes involved in small GTPases and were significantly correlated with the PI3K-AKT, MAPK, RAS, and WNT oncogenic pathways. On the other hand, global DNA hypermethylation was preferentially associated with recurrent HGSOC. A total of 2969 DMPs corresponding to 1471 genes were involved in olfactory transduction, and calcium and cAMP signaling. Co-treatment with demethylating agents showed significant growth retardation in ovarian cancer cells through differential inductions, such as cell apoptosis by azacytidine or G2/M cell cycle arrest by decitabine and thioguanine. Notably, azacytidine and decitabine, though not thioguanine, synergistically enhanced cisplatin-mediated cytotoxicity in HGSOC cells.

CONCLUSIONS

This study demonstrates the significant association of global hypermethylation with poor prognosis and drug resistance in high-grade EOC and highlights the potential of demethylating agents in cancer treatment.

Obesity, an independent predictor of pre and postoperative tumor grading disagreement in endometrial cancer

INTRODUCTION

Obesity is a known independent risk factor for endometrial cancer (EC), and obese patients have a 4.7-fold increased risk compared to the general population to develop the neoplasm. To date, a general pre and postoperative tumor grading agreement from 53 % to 82 % is reported for endometrial analysis, and a consensus on which factors might influence the tumor grading discordance is still absent. Furthermore, although obesity alters the endometrial microenvironment, no studies investigated the role of obesity in the grading agreement of EC patients. This study aims to analyze the role of obesity in the pre and postoperative tumor grading agreement.

MATERIALS AND METHODS

A retrospective analysis was conducted on EC cancer women subjected to surgical treatment. Upgrading discordance was defined as higher tumor grading on final pathological analysis compared to tumor grading on the preoperative examination. Downgrading discordance was defined as a lower tumor grading at the postoperative surgical specimen analysis compared to the preoperative biopsy.

RESULTS

Of the 293 selected patients, 245 were included in the analysis. One hundred and forty nine (60.8 %) patients were tumor grade G1, 52 (21.2 %) G2, and 44 (18.0 %) G3. Grading agreement was 83.9 % for G1 patients, 51.9 % for G2 patients, and 83.3 % for G3 patients. The multivariate analysis showed obesity (BMI > 30 kg/m²) as significant factor influencing pre and postoperative grading agreement (p = 0.014, Odds Ratio 2.036, 95 % Confidence Interval 1.141-3.635).

CONCLUSIONS

Our study for the first time showed obesity as the only factor in the multivariate analysis lowering the pre and postoperative tumor grading concordance. Grade 2 tumor was the factor that most frequently disagreed with the final surgical specimen analysis both in the general and in obese patients.

Noninvasively Evaluating the Grading of Glioma by Multiparametric Magnetic Resonance Imaging

RATIONALE AND OBJECTIVE

To investigate the performance of multi-parametric magnetic resonance imaging (MRI) for glioma grading.

MATERIALS AND METHODS

Seventy consecutive patients with histopathologically confirmed glioma were retrospectively evaluated by conventional MRI, dynamic susceptibility-weighted contrast-enhanced, multiple diffusion-weighted imaging signal models including mono-exponential, bi-exponential, stretched exponential, and diffusion kurtosis imaging. One-way analysis of variance and independent-samples t test were used to compare the MR parameter values between low and high grades as well as among all grades of glioma. Receiver operating characteristic analysis, Spearman's correlation analysis, and binary logistic regression analysis were used to assess their diagnostic performance.

RESULTS

The diagnostic performance (the optimal thresholds, area under the receiver operating characteristic curve, sensitivity, and specificity) was achieved with normalized relative cerebral blood flow (rCBV) (2.240 ml/100 g, 0.844, 87.8%, and 75.9%, respectively), mean kurtosis (MK) (0.471, 0.873, 92.7%, and 79.3%), and water molecular diffusion heterogeneity index (α) (1.064, 0.847, 79.3% and 78.0%) for glioma grading. There were positive correlations between rCBV and MK and the tumor grades and negative correlations between α and the tumor grades (p < 0.01). The parameter of α yielded a diagnostic accuracy of 85.3%, the combination of MK and α yielded a diagnostic accuracy of 89.7%, while the combination of rCBV, MK, and α were more accurate (94.2%) in predicting tumor grade.

CONCLUSION

The most accurate parameters were rCBV, MK, and α in dynamic susceptibility-weighted contrast, diffusion kurtosis imaging, and Multi-b diffusion-weighted imaging for glioma grading, respectively. Multiparametric MRI can increase the accuracy of glioma grading.

Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study

PURPOSE

To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.

MATERIALS AND METHODS

Preoperative computed tomography and tumor marker information of 617 primary liver cancer patients were retrospectively collected to develop CNN models categorizing tumors into three categories: moderately differentiated HCC (mHCC), poorly differentiated HCC (pHCC), and ICC, where the histopathological diagnoses were considered as ground truths. The models processed manually cropped tumor with and without tumor marker information (two-input and one-input models, respectively). Overall accuracy was assessed using a held-out dataset (10%). Area under the curve, sensitivity, and specificity for differentiating ICC from HCCs (mHCC + pHCC), and pHCC from mHCC were also evaluated. We assessed two radiologists' performance without tumor marker information as references (overall accuracy, sensitivity, and specificity). The two-input model was compared with the one-input model and radiologists using permutation tests.

RESULTS

The overall accuracy was 0.61, 0.60, 0.55, 0.53 for the two-input model, one-input model, radiologist 1, and radiologist 2, respectively. For differentiating pHCC from mHCC, the two-input model showed significantly higher specificity than radiologist 1 (0.68 [95% confidence interval: 0.50-0.83] vs 0.45 [95% confidence interval: 0.27-0.63]; p = 0.04).

CONCLUSION

Our CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

Correlation between contrast enhancement, standardized uptake value (SUV), and diffusion restriction (ADC) with tumor grading in patients with therapy-naive neuroendocrine neoplasms using hybrid 68Ga-DOTATOC PET/MRI

OBJECTIVES

To investigate a correlation between ⁶⁸Ga-DOTATOC PET/MR imaging parameters such as arterial and venous contrast enhancement, diffusion restriction, and maximum standardized uptake value (SUVmax) with histopathological tumor grading in patients with neuroendocrine neoplasms (NEN).

MATERIAL AND METHODS

A total of 26 patients with newly diagnosed, therapy-naive neuroendocrine neoplasms (NEN) were enrolled in this prospective study and underwent ⁶⁸Ga-DOTATOC PET/MRI. Images were evaluated regarding NEN lesion number and location, predominant tumor signal intensity on precontrast T1w and T2w images and on postcontrast arterial and portal venous phase T1w images, apparent diffusion coefficient (ADC) and SUVmax. Histopathological tumor grading was assessed and related to PET/MRI features using Pearson's correlation coefficient and Fisher's exact t-test. A binary logistic regression analysis was performed to evaluate a potential relation with an aggressive tumor biology and odds ratios (OR) were calculated.

RESULTS

There was a moderate negative correlation between arterial contrast enhancement and tumor grading (r=-0.35, p = 0.005), while portal venous enhancement showed a weak positive correlation with the Ki-67 index (r = 0.28, p = 0.008) and a non-significant positive correlation with tumor grading (r = 0.19, p = 0.063). Features that were significantly associated with an aggressive tumor biology were the presence of liver metastases (OR 2.6, p = 0.042), T1w hyperintensity in comparison to muscle (OR 12.7, p = 0.0001), arterial phase hyperenhancement (OR 1.4, p = 0.001), diffusion restriction (OR 2.8, p = 0.02) and SUVmax above the hepatic level (OR 7.0, p = 0.001).

CONCLUSION

The study reveals that PET/MRI features might be useful for prediction of NEN grading and thus provide a preliminary assessment of tumor aggressiveness.

Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

PURPOSE OF REVIEW

This review will explore the latest in advanced imaging techniques, with a focus on the complementary nature of multiparametric, multimodality imaging using magnetic resonance imaging (MRI) and positron emission tomography (PET).

RECENT FINDINGS

Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. PET performed in addition to advanced MRI provides complementary information regarding tumor metabolic properties, particularly when performed simultaneously. 18F-fluoroethyltyrosine (FET) PET improves the specificity of tumor diagnosis and evaluation of post-treatment changes. Incorporation of radiogenomics and machine learning methods further improve advanced imaging. The complementary nature of combining advanced imaging techniques across modalities for brain tumor imaging and incorporating technologies such as radiogenomics has the potential to reshape the landscape in neuro-oncology.

Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

PURPOSE

To prospectively investigate the capabilities of texture analysis (TA) based on apparent diffusion coefficient (ADC) map of the entire tumor volume and the whole volume of peri-tumoral edema, in discriminating between high-grade glioma (HGG) and low-grade glioma (LGG).

MATERIALS AND METHODS

A total of 33 patients with histopathological proven glioma were prospectively included. There were 20 men and 13 women with a mean age of 54.5±14.7 (standard deviation [SD]) years (range: 34-75years). TA parameters of whole tumor and peri-tumoral edema were extracted from the ADC map obtained with diffusion-weighted spin-echo echo-planar magnetic resonance imaging at 1.5-T. TA variables of HGG were compared to those of LGG. The optimum cut-off values of TA variables and their corresponding sensitivity, specificity and accuracy for differentiating between LGG and HGG were calculated using receiver operating characteristic curve analysis.

RESULTS

Mean and median tumoral ADC of HGG were significantly lower than those of LGG, at 1.23×10^-3 mm²/s and 1.21×10^-3 mm²/s cut-off values, yielding 70% sensitivity each (95% CI: 59-82% and 61-80%, respectively), 80% (95% CI: 79-98%) and 90% (95% CI: 82-97%) specificity, and 73% (95% CI: 66-91%) and 76% (95% CI: 72-90%) accuracy, respectively. Significant differences in tumoral and peri-tumoral kurtosis were found between HGG and LGG at 1.60 and 0.314 cut-off values yielding sensitivities of 74% (95% CI: 58-83%) and 70% (95% CI: 59-84%), specificities of 90% (95% CI: 80-95%) and 70% (95% CI: 64-83%) and accuracies of 79% (95% CI: 69-89%) and 70% (95% CI: 64-77%), respectively.

CONCLUSION

Measurements of whole tumoral and peri-tumoral TA, based on ADC maps, provide useful information that helps distinguish between HGG and LGG.

Comparison of amide proton transfer imaging and magnetization transfer imaging in revealing glioma grades and proliferative activities: a histogram analysis

PURPOSE

Comprehensive understanding glioma metabolic characters is of great help for patient management. We aimed to compare amide proton transfer imaging (APTw) and magnetization transfer imaging (MT) in predicting glioma malignancy and reflecting tumor proliferation.

METHODS

Thirty low-grade gliomas (LGGs) and 39 high-grade gliomas (HGGs) were prospectively included, of which 58 samples Ki-67 levels were quantified. Anatomical MRI, APTw, and MT were scanned, and magnetization transfer ratio (MTR) and asymmetric magnetic transfer ratio at 3.5 ppm (MTR_asym(3.5ppm)) were calculated. ROIs were semi-automatically drawn with ImageJ, from which histogram features, including 5th, 25th, 50th, mean, 70th, 90th, and 95th percentiles were extracted. The independent t test was used to test differences in LGGs and HGGs, and correlations between histogram features and tumor grades, Ki-67 were revealed by Spearman's rank or Pearson's correlation analysis.

RESULTS

The maximum correlation coefficient (R) values of APTw were 0.526 (p < 0.001) with tumor grades and 0.397 (p < 0.001) with Ki-67 at 90th percentiles, while only 5th and 25th percentiles of MT significantly correlated with tumor grades. Moreover, APTw features were significantly different in LGGs and HGGs, except 5th percentile. The most significantly different feature was 95th percentile, providing the excellent AUC of 0.808. However, the best feature in MTR was 5th percentiles with AUC of 0.703. Combing 5th and 95th of APTw achieved highest AUC Of 0.837.

CONCLUSIONS

Both APTw and MT provide quantitative information for tumor metabolite imaging. However, APTw supplys more specific information in reflecting glioma biological behaviors than MT, and well differentiates glioma malignancy.

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

INTRODUCTION

A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma.

METHODS

A multi-institutional study involving multiple cohorts of invasive pulmonary adenocarcinomas was conducted. A cohort of 284 stage I pulmonary adenocarcinomas was used as a training set to identify histologic features associated with patient outcomes (recurrence-free survival [RFS] and overall survival [OS]). Receiver operating characteristic curve analysis was used to select the best model, which was validated (n = 212) and tested (n = 300, including stage I-III) in independent cohorts. Reproducibility of the model was assessed using kappa statistics.

RESULTS

The best model (area under the receiver operating characteristic curve [AUC] = 0.749 for RFS and 0.787 for OS) was composed of a combination of predominant plus high-grade histologic pattern with a cutoff of 20% for the latter. The model consists of the following: grade 1, lepidic predominant tumor; grade 2, acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and grade 3, any tumor with 20% or more of high-grade patterns (solid, micropapillary, or complex gland). Similar results were seen in the validation (AUC = 0.732 for RFS and 0.787 for OS) and test cohorts (AUC = 0.690 for RFS and 0.743 for OS), confirming the predictive value of the model. Interobserver reproducibility revealed good agreement (k = 0.617).

CONCLUSIONS

A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.

A CT-based deep learning model for predicting the nuclear grade of clear cell renal cell carcinoma

PURPOSE

To investigate the effects of different methodologies on the performance of deep learning (DL) model for differentiating high- from low-grade clear cell renal cell carcinoma (ccRCC).

METHOD

Patients with pathologically proven ccRCC diagnosed between October 2009 and March 2019 were assigned to training or internal test dataset, and external test dataset was acquired from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. The effects of different methodologies on the performance of DL-model, including image cropping (IC), setting the attention level, selecting model complexity (MC), and applying transfer learning (TL), were compared using repeated measures analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis. The performance of DL-model was evaluated through accuracy and ROC analyses with internal and external tests.

RESULTS

In this retrospective study, patients (n = 390) from one hospital were randomly assigned to training (n = 370) or internal test dataset (n = 20), and the other 20 patients from TCGA-KIRC database were assigned to external test dataset. IC, the attention level, MC, and TL had major effects on the performance of the DL-model. The DL-model based on the cropping of an image less than three times the tumor diameter, without attention, a simple model and the application of TL achieved the best performance in internal (ACC = 73.7 ± 11.6%, AUC = 0.82 ± 0.11) and external (ACC = 77.9 ± 6.2%, AUC = 0.81 ± 0.04) tests.

CONCLUSIONS

CT-based DL model can be conveniently applied for grading ccRCC with simple IC in routine clinical practice.

Pediatric ovarian immature teratoma: Histological grading and clinical characteristics

BACKGROUND

Ovarian immature teratomas (ITs) are relatively rare among all pediatric ovarian tumors. The histological grading for ovarian ITs, which ranges from 1 to 3, is based on the proportion of immature neuroepithelial component. Higher-grade ITs in adults are treated as malignant neoplasms and require adjuvant chemotherapy. However, there is no consensus on the therapeutic management of pediatric ovarian ITs. The aim of our study was to analyze the histological grades and clinical characteristics of ovarian ITs in pediatric patients.

METHODS

This retrospective chart review consisted of seven patients, including one, three, and three patients with histological grade 1, 2, and 3 pediatric ovarian ITs, respectively, who were treated at our institute between 2000 and 2016. Collected data comprised age, alpha-fetoprotein (AFP) level, clinical stage, tumor size, treatment, and prognosis.

RESULTS

The median age and AFP levels of patients with grade 1, 2, and 3 ovarian ITs were 8, 7, and 10 years and 37, 112, and 221 ng/ml, respectively. All cases were Children Oncology Group (COG) stage I and International Federation of Gynecology and Obstetrics (FIGO) stage IA. All patients had unilateral tumors in the right ovary. The median tumor sizes of the grade 1, 2, and 3 IT patients were 104, 160, and 100 cm², respectively. All patients underwent primary open surgery alone. Two patients, including one patient each with grade 2 and 3 ITs, underwent tumor enucleation as ovary-sparing surgery, whereas the remaining five patients underwent unilateral salpingo-oophorectomy. The median follow-up was seven years, and all cases achieved event-free survival.

CONCLUSIONS

Clinical characteristics of patients with grade 3 ovarian ITs were relatively older and had higher AFP levels than those with lower-grade ITs. According to our patient's clinical course and prognosis, COG stage I pediatric ITs should be treated by surgery alone and that postoperative chemotherapy is unnecessary even for those with grade 3 ITs as well as patients with rather low AFP levels.

LEVEL OF EVIDENCE

IV.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics

OBJECTIVE

To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation.

RESULTS

Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC.

CONCLUSIONS

MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging.

KEY POINTS

• Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

[Resection margins in soft tissue sarcomas]

BACKGROUND

Soft tissue sarcomas (STS) are a rare and heterogeneous group of malignant tumors that arise from the mesenchymal tissue. STS can form anywhere in the human body, with the extremities being preferred sites of predilection.

TREATMENT

A fundamental pillar of treatment is the surgical resection of soft tissue sarcomas. The goal is always an R0 resection with a safety margin. There is no consensus in the literature about the desired tumor-free resection margin. The decisive factors for these resection margins are histopathology, presence of anatomical barriers (capsule, tendon, fascia, cartilage, periosteum) and possibilities of (neo-) adjuvant therapy.

DISCUSSION

References in the literature support the role of resection margins as a predictor of local recurrence. Regarding the role of resection margins in overall survival, available data is divergent. There are known prognostic factors that influence overall survival, such as histological subtype, tumor size, tumor grading, and presence of metastases. So far, several studies have attempted to quantify the margins of resection, but no consensus has been reached, and debates are ongoing. When analyzing all the results of the data in the literature, it seems appropriate to aim for a negative resection margin >1 mm including an anatomical border structure, if possible.

Tumor grading of soft tissue sarcomas using MRI-based radiomics

Background

Treatment decisions for multimodal therapy in soft tissue sarcoma (STS) patients greatly depend on the differentiation between low-grade and high-grade tumors. We developed MRI-based radiomics grading models for the differentiation between low-grade (G1) and high-grade (G2/G3) STS.

Methods

The study was registered at ClinicalTrials.gov (number NCT03798795). Contrast-enhanced T1-weighted fat saturated (T1FSGd), fat-saturated T2-weighted (T2FS) MRI sequences, and tumor grading following the French Federation of Cancer Centers Sarcoma Group obtained from pre-therapeutic biopsies were gathered from two independent retrospective patient cohorts. Volumes of interest were manually segmented. After preprocessing, 1394 radiomics features were extracted from each sequence. Features unstable in 21 independent multiple-segmentations were excluded. Least absolute shrinkage and selection operator models were developed using nested cross-validation on a training patient cohort (122 patients). The influence of ComBatHarmonization was assessed for correction of batch effects.

Findings

Three radiomic models based on T2FS, T1FSGd and a combined model achieved predictive performances with an area under the receiver operator characteristic curve (AUC) of 0.78, 0.69, and 0.76 on the independent validation set (103 patients), respectively. The T2FS-based model showed the best reproducibility. The radiomics model involving T1FSGd-based features achieved significant patient stratification. Combining the T2FS radiomic model into a nomogram with clinical staging improved prognostic performance and the clinical net benefit above clinical staging alone.

Interpretation

MRI-based radiomics tumor grading models effectively classify low-grade and high-grade soft tissue sarcomas.

Fund

The authors received support by the medical faculty of the Technical University of Munich and the German Cancer Consortium.

Use of quantitative T2 mapping for the assessment of renal cell carcinomas: first results

Background

Correct staging and grading of patients with clear cell renal cell carcinoma (cRCC) is of clinical relevance for the prediction of operability and for individualized patient management. As partial or radial resection with postoperative tumor grading currently remain the methods of choice for the classification of cRCC, non-invasive preoperative alternatives to differentiate lower grade from higher grade cRCC would be beneficial.

Methods

This institutional-review-board approved cross-sectional study included twenty-seven patients (8 women, mean age ± SD, 61.3 ± 14.2) with histopathologically confirmed cRCC, graded according to the International Society of Urological Pathology (ISUP). A native, balanced steady-state free precession T2 mapping sequence (TrueFISP) was performed at 1.5 T. Quantitative T2 values were measured with circular 2D ROIs in the solid tumor portion and also in the normal renal parenchyma (cortex and medulla). To estimate the optimal cut-off T2 value for identifying lower grade cRCC, a Receiver Operating Characteristic Curve (ROC) analysis was performed and sensitivity and specificity were calculated. Students’ t-tests were used to evaluate the differences in mean values for continuous variables, while intergroup differences were tested for significance with two-tailed Mann-Whitney-U tests.

Results

There were significant differences between the T2 values for lower grade (ISUP 1–2) and higher grade (ISUP 3–4) cRCC (p < 0.001), with higher T2 values for lower grade cRCC compared to higher grade cRCC. The sensitivity and specificity for the differentiation of lower grade from higher grade tumors were 83.3% (95% CI: 0.59–0.96) and 88.9% (95% CI: 0.52–1.00), respectively, using a threshold value of ≥110 ms. Intraobserver/interobserver agreement for T2 measurements was excellent/substantial.

Conclusions

Native T2 mapping based on a balanced steady-state free precession MR sequence might support an image-based distinction between lower and higher grade cRCC in a two-tier-system and could be a helpful addition to multiparametric imaging.

Electronic supplementary material

The online version of this article (10.1186/s40644-019-0222-8) contains supplementary material, which is available to authorized users.

Collagen organization of renal cell carcinoma differs between low and high grade tumors

Background

The traditional pathologic grading for human renal cell carcinoma (RCC) has low concordance between biopsy and surgical specimen. There is a need to investigate adjunctive pathology technique that does not rely on the nuclear morphology that defines the traditional grading. Changes in collagen organization in the extracellular matrix have been linked to prognosis or grade in breast, ovarian, and pancreatic cancers, but collagen organization has never been correlated with RCC grade. In this study, we used Second Harmonic Generation (SHG) based imaging to quantify possible differences in collagen organization between high and low grades of human RCC.

Methods

A tissue microarray (TMA) was constructed from RCC tumor specimens. Each TMA core represents an individual patient. A 5 μm section from the TMA tissue was stained with standard hematoxylin and eosin (H&E). Bright field images of the H&E stained TMA were used to annotate representative RCC regions. In this study, 70 grade 1 cores and 51 grade 4 cores were imaged on a custom-built forward SHG microscope, and images were analyzed using established software tools to automatically extract and quantify collagen fibers for alignment and density assessment. A linear mixed-effects model with random intercepts to account for the within-patient correlation was created to compare grade 1 vs. grade 4 measurements and the statistical tests were two-sided.

Results

Both collagen density and alignment differed significantly between RCC grade 1 and RCC grade 4. Specifically, collagen fiber density was greater in grade 4 than in grade 1 RCC (p < 0.001). Collagen fibers were also more aligned in grade 4 compared to grade 1 (p < 0.001).

Conclusions

Collagen density and alignment were shown to be significantly higher in RCC grade 4 vs. grade 1. This technique of biopsy sampling by SHG could complement classical tumor grading approaches. Furthermore it might allow biopsies to be more clinically relevant by informing diagnostics. Future studies are required to investigate the functional role of collagen organization in RCC.

A deep learning radiomics model for preoperative grading in meningioma

OBJECTIVES

To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.

METHODS

We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network. The random forest algorithm was used to select features with importance values over 0.001, upon which a deep learning signature was built by a linear discriminant analysis classifier. The performance of our DLR model was assessed by discrimination and calibration in the independent validation cohort. For comparison, a radiomic model based on hand-crafted features and a fusion model were built.

RESULTS

The DLR signature comprised 39 deep learning features and showed good discrimination performance in both the primary and validation cohorts. The area under curve (AUC), sensitivity, and specificity for predicting meningioma grades were 0.811(95% CI, 0.635-0.986), 0.769, and 0.898 respectively in the validation cohort. DLR performance was superior over the hand-crafted features. Calibration curves of DLR model showed good agreements between the prediction probability and the observed outcome of high-grade meningioma.

CONCLUSIONS

Using routine MRI data, we developed a DLR model with good performance for noninvasively individualized prediction of meningioma grades, which achieved a quantization capability superior over the hand-crafted features. This model has potential to guide and facilitate the clinical decision-making of whether to observe or to treat patients by providing prognostic information.

Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

OBJECTIVES

To determine the utility of the amide proton transfer-weighted MR imaging in differentiating the WHO grade and predict proliferative activity of meningioma.

METHODS

Fifty-three patients with WHO grade I meningiomas and 26 patients with WHO grade II meningiomas underwent conventional and APT-weighted sequences on a 3.0 Tesla MR before clinical intervention. The APT-weighted (APTw) parameters in the solid tumor region were obtained and compared between two grades using the t test; the receiver operating characteristic (ROC) curve was used to assess the best parameter for predicting the grade of meningiomas. Pearson's correlation coefficient was calculated between the APTw_max and Ki-67 labeling index in meningiomas.

RESULTS

The APTw_max and APTw_mean values were not significantly different between WHO grade I and grade II meningiomas (p = 0.103 and p = 0.318). The APTw_min value was higher and the APTw_max-min value was lower in WHO grade II meningiomas than in WHO grade I tumors (p = 0.027 and p = 0.019). But the APTw_min was higher and the APTw_max-min was lower in microcystic meningiomas than in WHO grade II meningiomas (p = 0.001 and p = 0.006). The APTw_min combined with APTw_max-min showed the best diagnostic performance in predicting the grade of meningiomas with an AUC of 0.772. The APTw_max value was positively correlated with Ki-67 labeling index (r = 0.817, p < 0.001) in meningiomas; the regression equation for the Ki-67 labeling index (%) (Y) and APTw_max (%) (X) was Y = 4.9 × X - 12.4 (R² = 0.667, p < 0.001).

CONCLUSION

As a noninvasive imaging method, the ability of APTw-MR imaging in differentiating the grade of meningiomas is limited, but the technology can be used to predict the proliferative activity of meningioma.

KEY POINTS

• The APTw _min value was higher and the APTw _max-min value was lower in WHO grade II meningioma than in grade I tumors. • The APTw _min value was higher and the APTw _max-min value was lower in microcystic meningiomas than in WHO grade II meningiomas. • The APTw _max value was positively correlated with meningioma proliferation index.

Comparison of Monitor-Image and Printout-Image Methods in Ki-67 Scoring of Gastroenteropancreatic Neuroendocrine Tumors

Gastroenteropancreatic neuroendocrine tumors (GEP-NET) are classified according to tumor grade. Ki-67 and mitotic count are the two determinants of this classification. Therefore, Ki-67 scoring becomes very important in classifying the patients accurately. Eye-balling, counting of cells through the microscope, automated image analysis systems, and manual counting of printed image are the four major scoring methods in use. The aim of this study is to show the agreement between monitor-image method (MIM) and printout-image method (PIM) of Ki-67 scoring. In our study, 120 GEP-NETs from 85 patients diagnosed between January 2005 and July 2017 were evaluated. Thirty-seven cases with either polypectomy or resection material were selected. Seven different scoring methods using either a monitor-image or a printout-image were applied for Ki-67 scoring. They are as follows: whole-PIM, 1/9-PIM, whole-MIM, 1/4-MIM, 1/6-MIM, 1/9-MIM, and 1/12-MIM. In the comparison of Ki-67 scoring methods, intraclass correlation coefficients ranging from 0.951 to 0.999 were found. The Bland-Altman analysis showed near-perfect agreement between whole-MIM and whole-PIM as well as 1/9-MIM and 1/9-PIM. The level of agreements among the other methods were sufficient too, but there was a relative decrease in the level of agreement as the area of counting becomes smaller. The average application time decreased from 373.7 to 41.7 s gradually as the scoring area becomes smaller. Our study shows that there is a remarkable agreement between the MIM and PIM used in Ki-67 scoring.

Mixed Invasive Ductal and Lobular Carcinoma of the Breast: Prognosis and the Importance of Histologic Grade

BACKGROUND

The diagnosis of mixed invasive ductal and lobular carcinoma (IDC-L) in clinical practice is often associated with uncertainty related to its prognosis and response to systemic therapies. With the increasing recognition of invasive lobular carcinoma (ILC) as a distinct disease subtype, questions surrounding IDC-L become even more relevant. In this study, we took advantage of a detailed clinical database to compare IDC-L and ILC regarding clinicopathologic and treatment characteristics, prognostic power of histologic grade, and survival outcomes.

MATERIALS AND METHODS

In this retrospective cohort study, we identified 811 patients diagnosed with early-stage breast cancer with IDC-L or ILC. Descriptive statistics were performed to compare baseline clinicopathologic characteristics and treatments. Survival rates were subsequently analyzed using the Kaplan-Meier method and compared using the Cox proportional hazards model.

RESULTS

Patients with ILC had more commonly multifocal disease, low to intermediate histologic grade, and HER2-negative disease. Histologic grade was prognostic for patients with IDC-L but had no significant discriminatory power in patients with ILC. Among postmenopausal women, those with IDC-L had significantly better outcomes when compared with those with ILC: disease-free survival (DFS) and overall survival (OS; adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.31-0.95). Finally, postmenopausal women treated with an aromatase inhibitor had more favorable DFS and OS than those treated with tamoxifen only (OS adjusted HR, 0.50; 95% CI, 0.29-0.87), which was similar for both histologic types (p = .212).

CONCLUSION

IDC-L tumors have a better prognosis than ILC tumors, particularly among postmenopausal women. Histologic grade is an important prognostic factor in IDC-L but not in ILC.

IMPLICATIONS FOR PRACTICE

This study compared mixed invasive ductal and lobular carcinoma (IDC-L) with invasive lobular carcinomas (ILCs) to assess the overall prognosis, the prognostic role of histologic grade, and response to systemic therapy. It was found that patients with IDC-L tumors have a better prognosis than ILC, particularly among postmenopausal women, which may impact follow-up strategies. Moreover, although histologic grade failed to stratify the risk of ILC, it showed an important prognostic power in IDC-L, thus highlighting its clinical utility to guide treatment decisions of IDC-L. Finally, the disease-free survival advantage of adjuvant aromatase inhibitors over tamoxifen in ILC was consistent in IDC-L.

Anisotropy of anomalous diffusion improves the accuracy of differentiating low- and high-grade cerebral gliomas

BACKGROUND

Anomalous diffusion model has been introduced and shown to be beneficial in clinical applications. However, only the directionally averaged values of anomalous diffusion parameters were investigated, and the anisotropy of anomalous diffusion remains unexplored. The aim of this study was to demonstrate the feasibility of using anisotropy of anomalous diffusion for differentiating low- and high-grade cerebral gliomas.

METHODS

Diffusion MRI images were acquired from brain tumor patients and analyzed using the fractional motion (FM) model. Twenty-two patients with histopathologically confirmed gliomas were selected. An anisotropy metric for the FM-related parameters, including the Noah exponent (α) and the Hurst exponent (H), was introduced and their values were statistically compared between the low- and high-grade gliomas. Additionally, multivariate logistic regression analysis was performed to assess the combination of the anisotropy metric and the directionally averaged value for each parameter. The diagnostic performances for grading gliomas were evaluated using a receiver operating characteristic (ROC) analysis.

RESULTS

The Hurst exponent H was more anisotropic in high-grade than in low-grade gliomas (P = 0.015), while no significant difference was observed for the anisotropy of α. The ROC analysis revealed that larger areas under the ROC curves were produced for the combination of α (1) and the combination of H (0.813) compared with the directionally averaged α (0.979) and H (0.594), indicating an improved performance for tumor differentiation.

CONCLUSION

The anisotropy of anomalous diffusion can provide distinctive information and benefit the differentiation of low- and high-grade gliomas. The utility of anisotropic anomalous diffusion may have an improved effect for investigating pathological changes in tissues.

Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading

BACKGROUND AND OBJECTIVE

To investigate the feasibility of histogram analysis of the T2*-based permeability parameter volume transfer constant (K^trans) for glioma grading and to explore the diagnostic performance of the histogram analysis of K^trans and blood plasma volume (v_p).

METHODS

We recruited 31 and 11 patients with high- and low-grade gliomas, respectively. The histogram parameters of K^trans and v_p, derived from the first-pass pharmacokinetic modeling based on the T2* dynamic susceptibility-weighted contrast-enhanced perfusion-weighted magnetic resonance imaging (T2* DSC-PW-MRI) from the entire tumor volume, were evaluated for differentiating glioma grades.

RESULTS

Histogram parameters of K^trans and v_p showed significant differences between high- and low-grade gliomas and exhibited significant correlations with tumor grades. The mean K^trans derived from the T2* DSC-PW-MRI had the highest sensitivity and specificity for differentiating high-grade gliomas from low-grade gliomas compared with other histogram parameters of K^trans and v_p.

CONCLUSIONS

Histogram analysis of T2*-based pharmacokinetic imaging is useful for cerebral glioma grading. The histogram parameters of the entire tumor K^trans measurement can provide increased accuracy with additional information regarding microvascular permeability changes for identifying high-grade brain tumors.

B2 adrenergic receptors and morphological changes of the enteric nervous system in colorectal adenocarcinoma

AIM

To study the morphology of the enteric nervous system and the expression of beta-2 adrenergic (B2A) receptors in primary colorectal cancer.

METHODS

In this study, we included forty-eight patients with primary colorectal cancer and nine patients for control tissue from the excision of a colonic segment for benign conditions. We determined the clinicopathological features and evaluated the immunohistochemical expression pattern of B2A receptors as well as the morphological changes of the enteric nervous system (ENS). In order to assess statistical differences, we used the student t-test for comparing the means of two groups and one-way analysis of variance with Bonferroni’s post hoc analysis for comparing the means of more than two groups. Correlations were assessed using the Pearson’s correlation coefficient.

RESULTS

B2A receptors were significantly associated with tumor grading, tumor size, tumor invasion, lymph node metastasis (P < 0.05), while there were no statistically significant associations with gender, CRC location and gross appearance (P > 0.05). We observed, on one hand, a decrease of the relative area for both Auerbach and Meissner plexuses with the increase of the tumor grading, and on the other hand, an increase of the relative area of other nervous elements not in the Meissner plexus or in the Auerbach plexus with the tumor grading. For G1 tumors we found that epithelial B2A area showed an inverse correlation with the Auerbach plexus areas [r(14) = -0.531, P < 0.05], while for G2 tumors, epithelial B2A areas showed an indirect variation with both the Auerbach plexus areas [r(14) = -0.453, P < 0.05] and the Meissner areas [r(14) = -0.825, P < 0.01]. For G3 tumors, the inverse dependence increased for both Auerbach [r(14) = -0.587, P < 0.05] and Meissner [r(14) = -0.934, P < 0.05] plexuses.

CONCLUSION

B2A receptors play an important role in colorectal carcinogenesis and can be utilized as prognostic factors. Furthermore, study of the ENS in colorectal cancer may lead to targeted molecular therapies.

Preoperative grading of supratentorial gliomas using high or standard b-value diffusion-weighted MR imaging at 3T

PURPOSE

The goal of this study was to compare diffusion-weighted magnetic resonance imaging (DW-MRI) using high b-value (b=3000s/mm²) to DW-MRI using standard b-value (b=1000s/mm²) in the preoperative grading of supratentorial gliomas.

MATERIALS AND METHODS

Fifty-three patients with glioma had brain DW-MRI at 3T using two different b-values (b=1000s/mm² and b=3000s/mm²). There were 35 men and 18 women with a mean age of 40.5±17.1 years (range: 18-79 years). Mean, minimum, maximum, and range of apparent diffusion coefficient (ADC) values for solid tumor ROIs (ADC_mean, ADC_min, ADC_max, and ADC_diff), and the normalized ADC (ADC_ratio) were calculated. A Kruskal-Wallis statistic with Bonferroni correction for multiple comparisons was applied to detect significant ADC parameter differences between tumor grades by including or excluding 19 patients with an oligodendroglioma. Receiver operating characteristic curve analysis was conducted to define appropriate cutoff values for grading gliomas.

RESULTS

No differences in ADC derived parameters were found between grade II and grade III gliomas. Mean ADC values using standard b-value were 1.17±0.27×10^-3mm²/s [range: 0.63-1.61], 1.05±0.22×10^-3mm²/s [range: 0.73-1.33], and 0.86±0.23×10^-3mm²/s [range: 0.52-1.46] for grades II, III and IV gliomas, respectively. Using high b-value, mean ADC values were 0.89±0.24×10^-3mm²/s [range: 0.42-1.25], 0.82±0.20×10^-3mm²/s [range: 0.56-1.10], and 0.59±0.17×10^-3mm²/s [range: 0.40-1.01] for grades II, III and IV gliomas, respectively. ADC_mean, ADC_ratio, ADC_max, and ADC_min were different between grade II and grade IV gliomas at both standard and high b-values. Differences in ADC_mean, ADC_max, and ADC_diff were found between grade III and grade IV only using high b-value.

CONCLUSION

ADC parameters derived from DW-MRI using a high b-value allows a better differential diagnosis of gliomas, especially for differentiating grades III and IV, than those derived from DW-MRI using a standard b-value.

Magnetic Resonance Spectroscopy and Single-Photon Emission Computed Tomography in the Evaluation of Cerebral Tumors: A Case Report

In their daily clinical practice, physicians have to confront diagnostic dilemmas which cannot be resolved by the application of only one imaging technique. In this case report, we present a 66-year-old woman who was admitted to our institution for the surgical resection of a recently diagnosed brain tumor. The patient had a history of epileptic seizures and was hospitalized in the past for anti-phospholipid syndrome related to a non-Hodgkin lymphoma in remission. Magnetic resonance imaging (MRI) examination revealed an enhancing right parasagittal lesion with significant edema suggestive of a high grade glioma. Advanced MRI techniques including proton magnetic resonance spectroscopy (¹H-MRS) showed findings compatible of glioma. An additional examination was performed as part of a protocol that we are routinely performing in our institution for all brain tumors including not only the gold standard advanced MRI techniques but also single-photon emission computed tomography (SPECT) with technetium-99m (Tc99m). Brain SPECT indicated the presence of a meningioma which was verified by the histopathology of the resected specimen. In conclusion, a multimodality approach for the pre-surgical assessment of brain tumors has significant advantages not only for the diagnosis but also for the evaluation of intracranial tumors histology.

A fractional motion diffusion model for grading pediatric brain tumors

Objectives

To demonstrate the feasibility of a novel fractional motion (FM) diffusion model for distinguishing low- versus high-grade pediatric brain tumors; and to investigate its possible advantage over apparent diffusion coefficient (ADC) and/or a previously reported continuous-time random-walk (CTRW) diffusion model.

Materials and methods

With approval from the institutional review board and written informed consents from the legal guardians of all participating patients, this study involved 70 children with histopathologically-proven brain tumors (30 low-grade and 40 high-grade). Multi-b-value diffusion images were acquired and analyzed using the FM, CTRW, and mono-exponential diffusion models. The FM parameters, D_fm, φ, ψ (non-Gaussian diffusion statistical measures), and the CTRW parameters, D_m, α, β (non-Gaussian temporal and spatial diffusion heterogeneity measures) were compared between the low- and high-grade tumor groups by using a Mann-Whitney-Wilcoxon U test. The performance of the FM model for differentiating between low- and high-grade tumors was evaluated and compared with that of the CTRW and the mono-exponential models using a receiver operating characteristic (ROC) analysis.

Results

The FM parameters were significantly lower (p < 0.0001) in the high-grade (D_fm: 0.81 ± 0.26, φ: 1.40 ± 0.10, ψ: 0.42 ± 0.11) than in the low-grade (D_fm: 1.52 ± 0.52, φ: 1.64 ± 0.13, ψ: 0.67 ± 0.13) tumor groups. The ROC analysis showed that the FM parameters offered better specificity (88% versus 73%), sensitivity (90% versus 82%), accuracy (88% versus 78%), and area under the curve (AUC, 93% versus 80%) in discriminating tumor malignancy compared to the conventional ADC. The performance of the FM model was similar to that of the CTRW model.

Conclusions

Similar to the CTRW model, the FM model can improve differentiation between low- and high-grade pediatric brain tumors over ADC.

•

The fractional motion (FM) diffusion model was applied to pediatric brain tumors.

•

The FM model parameters can be sensitive to tissue microstructures.

•

The FM model outperforms the mono-exponential diffusion model.

•

The FM model performs similarly to the continuous-time random-walk (CTRW) model.

•

Our results challenge those from recent biophysics studies in cell cultures.

Prospective evaluation of Ki-67 system in histological grading of soft tissue sarcomas in the Japan Clinical Oncology Group Study JCOG0304

Background

The correct clinical staging of soft tissue sarcomas (STS) is critical for the selection of treatments. The staging system consists of histological grade of the tumors and French Federation of Cancer Center (FNCLCC) system based on mitotic count is widely used for the grading. In this study, we compared the validity and usefulness of Ki-67 grading system with FNCLCC system in JCOG0304 trial which investigated the efficacy and safety of perioperative chemotherapy with doxorubicin and ifosfamide for STS.

Methods

All 70 eligible patients with STS in the extremities treated by perioperative chemotherapy in JCOG0304 were analyzed. Univariate and multivariate Cox regression analyses were conducted to investigate an influence on overall survival.

Results

The reproducibility of Ki-67 grading system in the histological grading of STS was higher than FNCLCC system (κ = 0.54 [95 % CI 0.39–0.71], and 0.46 [0.32–0.62], respectively). Although FNCLCC grade was not associated with overall survival (OS) in univariate analysis (HR 2.80 [0.74–10.55], p = 0.13), Ki-67 grading system had a tendency to associate with OS in univariate analysis (HR 4.12 [0.89–19.09], p = 0.07) and multivariate analysis with backward elimination (HR 3.51 [0.75–16.36], p = 0.11).

Conclusions

This is the first report demonstrating the efficacy of Ki-67 grading system for the patients with STS in the prospective trial. The results indicate that Ki-67 grading system might be useful for the evaluation of histological grade of STS.

Poorly differentiated clusters (PDC) in colorectal cancer: what is and ought to be known

Background

The counting of poorly differentiated clusters of 5 or more cancer cells lacking a gland-like structure in a tumor mass has recently been identified among the histological features predictive of poor prognosis in colorectal cancer.

Main body

Poorly differentiated clusters can easily be recognized in the histological sections of colorectal cancer routinely stained with haematoxylin and eosin. Despite some limitations related to specimen fragmentation, counting can also be assessed in endoscopic biopsies. Based on the number of poorly differentiated clusters that appear under a microscopic field of a ×20 objective lens (i.e., a microscopic field with a major axis of 1 mm), colorectal cancer can be graded into malignancies as follows: tumors with <5 clusters as grade 1, tumors with 5 to 9 clusters as grade 2, and tumors with ≥10 clusters as grade 3. High poorly differentiated cluster counts are significantly associated with peri-neural and lympho-vascular invasion, the presence of nodal metastases or micrometastases, as well as shorter overall and progression free survival to colorectal cancer.

Conclusion

The morphological aspects and clinical relevance of poorly differentiated clusters counting in colorectal cancer are discussed in this review.

Glioma Grading Using Cell Nuclei Morphologic Features in Digital Pathology Images

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold cross-validation confirms the efficacy of the proposed method.

Pathodiagnostic parameters and evaluation of O⁶- methyl guanine methyl transferase gene promoter methylation in meningiomas

Histopathological evaluation and grading of meningioma give important prognostic information. We evaluated retrospectively monotonous sheeting, necrosis, hypercellularity, nuclear pleomorphism, small cell changes, brain invasion, mitosis, mast cells, psammoma bodies, MIB-1 labeling index (MIB-1 LI) and histological grade of 230 primary meningioma tumors according to the latest World Health Organization (WHO) classification. To reveal any possible association between clinical features and promoter hypermethylation of O(6)-methylguanine-DNA methyltransferase (MGMT) as an important epigenetic modification in many human cancers, we also evaluated the methylation status of MGMT in meningiomas by a SYBR-green-based real-time PCR method. There was a female predominance (2.38 to 1) in the meningiomas. The mean age of the patients was 49.9 ± 12.6 years (range 16 to 78 years). Transitional meningiomas were the most common subtype of the meningiomas (35.21%, n=81). Most of the meningiomas were located in the falx and parasagital area. There was a significant correlation between histopathological features of malignancy. These features were observed more frequently and with statistical relation to grade II rather than grade I. Mast cells, psammoma bodies and nuclear pleomorphism had poor associations (P>0.05). When we re-evaluated the tumor grading, 31 patients with grade I meningiomas were upgraded to grade II. None of the meningiomas tested by MSQP were methylated in MGMT promoter sequence. High MIB-1 LI could be indicative for higher grade of meningioma. Continuous revision of the classification system is needed to improve the accuracy of prognostic judgments in meningioma. The data confirm that there is no rationale to test meningiomas for MGMT methylation status.