Cancer Grade Research Articles

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring.

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

EGFR mutation impacts recurrence in high-risk early-stage lung adenocarcinoma in the IASLC grading system.

The developments of perioperative treatments for patients with high-risk early-stage lung cancer are ongoing, however, real-world data and evidence of clinical significance of genetic aberration are lacking in this population. This study aimed to identify patients with early-stage lung adenocarcinoma at high risk for recurrence based on pathological indicators of poor prognosis, including the International Association for the Study of Lung Cancer (IASLC) grade, and elucidate the prognostic impact of epidermal growth factor receptor mutation (EGFRm) status. This retrospective study included 494 consecutive patients who underwent complete resection for pathological stage I lung adenocarcinoma between 2011 and 2016. The patients were evaluated for EGFRm and IASLC grade. Multivariable analysis was used to identify pathological factors for poor prognosis associated with recurrence-free survival (RFS) and overall survival (OS). Patients with any one of these factors were classified into the high-risk group. The prognostic impact of EGFRm was evaluated using RFS, OS, and cumulative recurrence proportion. Multivariable analysis for RFS and OS revealed that IASLC grade 3, pathological invasion size>2 cm, and presence of lymphovascular invasion were indicators of poor prognosis. EGFRm-positive patients had a higher incidence of all types of recurrence, including central nervous system (CNS) metastasis and distant metastasis in high-risk group, but not in low-risk group. This study provides evidence that patients with EGFRm-positive stage I lung adenocarcinoma in the high-risk group have an increased risk of recurrence, including CNS metastasis. These findings highlight the need for development of adjuvant treatment in this population.

Brain Cancer

Cancer is a significant threat to public health and a leading cause of morbidity across the globe. Of all cancers, brain cancer can be particularly catastrophic as treatment often fails to achieve the desired degree of effectiveness and diagnosis remains associated with a high mortality rate. Africa, as a continent with resource-limited countries, needs to allocate the necessary proper healthcare infrastructure to significantly reduce cancer rates and improve patient survival. A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model.

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor. We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness. Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively. Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

Changes in breast cancer grade from biopsy to excision following surgery or primary chemotherapy.

To compare histological grade (G) of breast cancer and its components (scores for tubule formation - T, nuclear pleomorphism - P and mitotic counts - M) in core needle biopsies (CNBs) and surgical excision specimens (EXC) in patients treated with primary surgery (CHIR) or primary chemotherapy (PST). Grade of matched pairs of carcinomas in CNB and EXC was assessed according to the Nottingham grading system. PST cases tended to have higher pretreatment G. Concordance rates in the CHIR (n = 760) and PST (n = 148) groups for T, P, M and G were 79%, 70%, 75%, 71% and 77%, 70%, 50%, 62%, respectively; differences in concordance rates were significant in M (p < 0.0001) and G (p = 0.024). For discordant cases in the CHIR group, CNBs tended to overestimate T and underestimate P, M and G, whereas in the PST group, the same trends were identified for T and P, but there was a significant tendency for M and G to be lower in EXC specimens. The reversal of M and G underestimation in CNB to "overestimation" in the PST group can only be explained with the effect of mitosis reduction following chemotherapy. Whether the posttreatment decrease in G reflects any prognostic value remains to be elucidated.

The Correlation Between Essential Amino Acid Tryptophan, Lysine, Phenylalanine and Chemotherapy of Breast Cancer.

To investigate the differences in serum tryptophan, lysine, and phenylalanine levels in breast cancer patients, the correlation between the three amino acids with the chemotherapy regimen, and their significance in the clinical diagnosis and treatment of breast cancer.Clinical data were collected from the Department of Breast Surgery at Yunnan Cancer Hospital, encompassing 216 cases from July to December 2020, including 91 healthy individuals, 38 with benign tumors, and 87 with cancer. Amino acid levels were measured using liquid chromatography-tandem mass spectrometry. Statistical analyses, such as the Kruskal-Wallis H-test and Wilcoxon test, were conducted to compare the levels of these amino acids across the healthy group, benign tumor group, and breast cancer group. The χ2 test and Fisher's exact probability method were employed to assess the relationship between amino acid levels and breast cancer stage, grade, and chemotherapy regimen.The results indicated that there were significant differences in serum lysine (H = 36.13, P < .001) and phenylalanine (H = 34.03, P < .001) levels among the three groups. However, tryptophan levels did not show statistically significant variances. Specifically, lysine and phenylalanine levels were significantly different when comparing the healthy group with the breast cancer group and the benign tumor group with the breast cancer group. These differences were not significant when comparing the healthy group with the benign tumor group. Furthermore, there were no statistically significant distinctions observed in lysine (F = 0.836, P > .05) and phenylalanine (F = 1.466, P > .05) levels across different conventional chemotherapy regimens among the breast cancer cases studied.Serum lysine and phenylalanine levels might serve as potential biomarkers for breast cancer, and the choice of chemotherapy regimen is unlikely to impact significant changes in these amino acid levels.

Comparison of pathologist and deep learning–based prostate cancer grading for prediction of metastatic outcomes in primary prostate cancer.

345 Background: Gleason grading is the most potent prognostic variable in primary prostate cancer, however inter-observer variability remains a major issue, particularly where subspecialty-trained pathologists are not available. Artificial intelligence algorithms for prostate cancer grading may improve health care equity by ensuring widespread access to standardized, high quality grading, however most algorithms have not been tested for performance with respect to oncologic outcomes. Here, we compared deep learning-based and pathologist-based Gleason grading for prediction of metastatic outcome in three large radical prostatectomy cohorts. Methods: Three previously published radical prostatectomy cohorts from Johns Hopkins were utilized for this study. The Natural History Cohort (n=318, PMID: 26058959) and Case-Cohort (n=231, PMID: 36006048) both utilized a case-cohort design on the outcome of metastasis, while the Race Cohort (n=335, PMID: 30851334) was designed for a grade-matched comparison of self-identified White and Black patients. For each cohort, a single representative H&amp;E-stained slide from the dominant tumor nodule was scanned and Gleason Grade Group (GG) assessed by a modified deep learning-based grading algorithm (PMID: 35233002), or by a subspecialty-trained pathologist. Harrell’s C-indices based on unadjusted Cox models for time to metastasis were compared for the pathologist- and deep learning-assigned GG in each cohort. Results: The C-index for deep learning-assigned GG in the Natural History Cohort was 0.757 (95% confidence interval [CI]: 0.703-0.811) compared to 0.748 (95% CI: 0.692-0.804) for the pathologist-assigned GG. The C-index for deep learning-assigned GG in the Case-Cohort was 0.848 (95% CI: 0.727-0.969) compared to 0.843 (95% CI: 0.722-0.964) for the pathologist-assigned GG. The C-index for deep learning-assigned GG in the Race Cohort was 0.754 (95% CI: 0.697-0.810) compared to 0.780 (95% CI: 0.717-0.844) for the pathologist-assigned GG. In a combined analysis of all three cohorts, the C-index for the deep learning-assigned GG was 0.765 (95% CI: 0.725-0.805) compared to 0.772 (95% CI: 0.730-0.814) for the pathologist-assigned grade group. In the combined cohorts, the historical pathologist-assigned GG for the entire case had a C-index of 0.796 (95% CI: 0.757-0.836). Conclusions: Deep learning and subspecialty-trained pathologist grading were highly comparable for association with metastatic outcome across three large radical prostatectomy cohorts.

Application of artificial intelligence (AI) features of nuclear morphology from BLASST-1 (Bladder Cancer Signal Seeking Trial) of nivolumab, gemcitabine, and cisplatin in patients with MIBC undergoing cystectomy.

674 Background: BLASST-1 is a multi-center phase II trial evaluating neoadjuvant nivolumab (N) with gemcitabine-cisplatin (GC) for patients (pts) with MIBC undergoing radical cystectomy (RC) (NCT03294304). 41 pts with MIBC (cT2-T4a, N≤1, M0) were enrolled between Feb 2018 and June 2019; (cT2N0 90%, cT3N0 7%, cT4N1 3%). Pts received C (70mg/m2) IV on D1, G (1000mg/m2) on D1, D8, and N (360 mg) IV on D8 every 21 days for 4 cycles followed by RC within 8 wks. The primary endpoint was pathologic downstaging (PaR; ≤pT1N0). Safety, Relapse-free survival (RFS), Progression-free survival (PFS) and biomarker analyses were secondary endpoints. PaR rate was 65.8%, the pCR (≤pT is N0) rate was 49% and there were no safety concerns or delays to RC. Morphometric characteristics of the cell nucleus can be used to assess bladder cancer grading and gain insights into cellular functionalities. In this study, we sought to evaluate the ability of the AI model to identify non-responders to neoadjuvant chemo-immunotherapy. in the BLASST-1 cohort based on computerized image features of nuclear morphology and architecture on pre-treatment transurethral resection of bladder tumor (TURBT) tissues. Methods: Of the 41 pts, we had H&amp;E images available for 34 pts, of which 23 had PaR and 11 did not have PaR and these were classified as responder (R) and non-responder (NR) groups. A machine learning model (U-net) was developed and invoked for tumor segmentation on the H&amp;E images from the BLASST-1 cohort. A second machine learning model (HoVer-Net) was employed to segment and classify individual nuclei. A total of 408 features relating to the textural and spatial arrangement of individual cancer nuclei were extracted. The 17 most significant features, identified through the least absolute shrinkage and selection operator, were used to train a Cox regression model to predict the risk of death using 361 MIBC pts from the Cancer Genome Atlas (TCGA). This Cox model was then applied to assign a risk score to pts in BLASST-1, using a threshold learned from TCGA pts, the individual pts in BLASST-1 were assigned as either low-risk or high-risk. Results: The top identified prognostic features described the textural appearance of individual nuclei with more texture. This model accurately predicted PaR in BLASST-1, with an area under a receiver operating characteristic curve of 0.83. Overall, the pts with the same nuclear angle direction within the tumor tissue had a high chance of response to the neoadjuvant chemo-IO combination in the BLASST-1 trial. Conclusions: A computerized AI model relying on nuclear morphologic and architecture features demonstrated prognostic capability in MIBC within the TCGA dataset and predictive capability for the PaR in the BLASST-1 trial. These findings support further validation studies.

Complementary biomarkers of computed tomography for diagnostic grading of gastric cancer: DSCC1 and GINS1.

Computed tomography (CT) is an important tool for grading gastric cancer. Gastric cancer typically originates from epithelial cells of gastric mucosa. However, complementary markers for gastric cancer, relationship between DSCC1, GINS1 and gastric cancer remain unclear. Gastric cancer data were obtained from gene expression omnibus (GEO). Differentially expressed genes (DEGs) were identified, weighted gene co-expression network analysis (WGCNA) was conducted. Protein-protein interaction (PPI) network was constructed and analyzed. Functional enrichment analysis, gene set enrichment analysis (GSEA), gene expression heatmaps, immune infiltration analysis were performed. The most relevant diseases related to core genes were identified using Comparative Toxicogenomics Database (CTD). TargetScan was used to screen miRNAs. Validation was carried out using Western blotting (WB) and reverse transcription-polymerase chain reaction (RT-PCR). 1243 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses revealed significant enrichment in cell cycle regulation, macrophage migration control, basement membrane, extracellular regions, growth factor binding, protein complex binding, P53 signaling pathway, protein digestion and absorption, metabolic pathways. Immune infiltration analysis indicated that high expression of activated Mast cells and Neutrophils, with a strong positive correlation between them, may influence progression of gastric cancer. CTD analysis revealed associations between DSCC1, GINS1 and gastric tumors, gastrointestinal diseases, tumors, gastritis, inflammation, necrosis. WB and RT-PCR results demonstrated high expression of DSCC1 and GINS1 in gastric cancer. The expressions of DSCC1 and GINS1 are up-regulated in gastric cancer, which can be used as supplementary markers for CT diagnostic grading of gastric cancer.

The BHLHE40‒PPM1F‒AMPK pathway regulates energy metabolism and is associated with the aggressiveness of endometrial cancer

BHLHE40 is a basic helix-loop-helix transcription factor that is involved in multiple cell activities including differentiation, cell cycle, and epithelial-to-mesenchymal transition. While there is growing evidence to support the functions of BHLHE40 in energy metabolism, little is known about the mechanism. In this study, we found that BHLHE40 expression was downregulated in cases of endometrial cancer of higher grade and advanced disease. Knockdown of BHLHE40 in endometrial cancer cells resulted in suppressed oxygen consumption and enhanced extracellular acidification. Suppressed pyruvate dehydrogenase (PDH) activity and enhanced lactated dehydrogenase (LDH) activity was observed in the knockdown cells. Knockdown of BHLHE40 also led to dephosphorylation of AMPKα Thr172 and enhanced phosphorylation of pyruvate dehydrogenase E1 subunit alpha 1 (PDHA1) Ser293 and lactate dehydrogenase A (LDHA) Tyr10. These results suggested that BHLHE40 modulates PDH and LDH activity by regulating the phosphorylation status of PDHA1 and LDHA. We found that BHLHE40 enhanced AMPKα phosphorylation by directly suppressing the transcription of an AMPKα-specific phosphatase, PPM1F. Our immunohistochemical study showed that the expression of BHLHE40, PPM1F, and phosphorylated AMPKα correlated with the prognosis of endometrial cancer patients. Because AMPK is a central regulator of energy metabolism in cancer cells, targeting the BHLHE40‒PPM1F‒AMPK axis may represent a strategy to control cancer development.

Sparse ordinal discriminant analysis.

Ordinal class labels are frequently observed in classification studies across various fields. In medical science, patients' responses to a drug can be arranged in the natural order, reflecting their recovery postdrug administration. The severity of the disease is often recorded using an ordinal scale, such as cancer grades or tumor stages. We propose a method based on the linear discriminant analysis (LDA) that generates a sparse, low-dimensional discriminant subspace reflecting the class orders. Unlike existing approaches that focus on predictors marginally associated with ordinal labels, our proposed method selects variables that collectively contribute to the ordinal labels. We employ the optimal scoring approach for LDA as a regularization framework, applying an ordinality penalty to the optimal scores and a sparsity penalty to the coefficients for the predictors. We demonstrate the effectiveness of our approach using a glioma dataset, where we predict cancer grades based on gene expression. A simulation study with various settings validates the competitiveness of our classification performance and demonstrates the advantages of our approach in terms of the interpretability of the estimated classifier with respect to the ordinal class labels.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer

BackgroundHistological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance.MethodsThis observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort.ResultsWe observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18–5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07–4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade.ConclusionRoutine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

Histological differentiation between sporadic and colitis-associated intestinal cancer in a nationwide study: A propensity-score-matched analysis.

Colitis-associated intestinal cancer (CAC) can develop in patients with inflammatory bowel disease; however, the malignant grade of CAC may differ from that of sporadic colorectal cancer (CRC). Therefore, we compared histological findings distinct from cancer stage between CAC and sporadic CRC to evaluate the features of CAC. We reviewed the clinical and histological data collected from a nationwide database in Japan between 1983 and 2020. Patient characteristics were compared to distinguish ulcerative colitis (UC), Crohn's disease (CD), and sporadic CRC. Comparisons were performed by using all collected data and propensity score-matched data. A total of 1077 patients with UC-CAC, 297 with CD-CAC, and 136927 with sporadic CRC were included. Although the prevalence of well or moderately differentiated adenocarcinoma (Tub1 and Tub2) decreased according to tumor progression for all diseases (P<0.01), the prevalence of other histological findings, including signet ring cell carcinoma, mucinous carcinoma, poorly differentiated adenocarcinoma, or squamous cell carcinoma, was significantly higher in CAC than in sporadic CRC. Based on propensity score-matched data for 982 patients with UC and 268 with CD, the prevalence of histological findings other than Tub1 and Tub2 was also significantly higher in those with CAC. At pT4, mucinous carcinoma occurred at a significantly higher rate in patients with CD (45/86 [52.3%]) than in those with sporadic CRC (13/88 [14.8%]) (P<0.01). CAC, including early-stage CAC, has a higher malignant grade than sporadic CRC, and this difference increases in significance with tumor progression.

Measurement of chromosomal instability and level of DNA damage in peripheral blood mononuclear cells of endometrial cancer patients.

Endometrial cancer is one of the most common invasive gynecologic malignancies in developed countries. The aim of this study was to evaluate chromosomal instability and level of DNA damage in peripheral blood mononuclear cells (PBMCs) of newly diagnosed endometrial cancer patients in relation to health status (diagnosis), age, histological grade of cancer, residence, smoking, number of pregnancies, miscarriages, and abortions. The analyzed sample consisted of 60 individuals, 30 endometrial cancer patients with an average age of 64.37 ± 7.08, and 30 healthy control women with an average age of 60.23 ± 11.55. Chromosomal instability was evaluated by the cytokinesis-block micronucleus (CBMN) assay, and the level of DNA damage by the single-cell gel electrophoresis (comet) assay in PBMCs. The average frequencies of micronuclei (MNi), nucleoplasmic bridges (NPBs) as well as nuclear buds (NBUDs) were significantly higher in cancer patients compared to controls (P < .0005). There was no difference in the nuclear division index (NDI) among the analyzed samples. The comet assay showed that the patients had a significantly increased genetic damage index (GDI) compared with controls (P < .0005). Using linear regression analysis, we found that health status (diagnosis) had the strongest influence on the MN frequency as well as GDI (P < .0005). Our results indicated that there is a high level of genetic damage in both the level of DNA and the level of chromosomes in the PBMCs of newly diagnosed patients with endometrial cancer, where the frequency and level of damage were significantly affected by health status, grade of cancer, residence, number of pregnancies, miscarriages, and abortions.

Metabolomic profiling of human bladder tissue extracts.

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified. Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers. This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients. Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853. To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Apparent diffusion coefficient in intrahepatic cholangiocarcinoma diffusion-weighted magnetic resonance imaging noninvasively predicts Ki-67 expression.

Tumor Ki-67 expression reflects prognosis and cancer grade, and biopsy-based preoperative assessment of Ki-67 expression is key to treatment. Apparent diffusion coefficient (ADC) values obtained with this imaging may noninvasively predict Ki-67 by reflecting tumor cell density and limited water molecule movement from irregular alignment. This study aimed to investigate the ability of ADC values to predict Ki-67 expression in intrahepatic cholangiocarcinoma (ICC). We retrospectively analyzed 39 cases of ICC confirmed by surgical pathology. All patients had undergone magnetic resonance imaging, and ADC values (mean, minimum, and maximum) were calculated. Ki-67 expression was assessed by immunohistochemistry, and patients were divided into groups of high (n=18) and low (n=21) Ki-67 expression. To assess the diagnostic performance of the ADC values for Ki-67 expression, we used the receiver operating characteristic curve and compared the areas under the curve (AUC). The mean and minimum ADC values were significantly lower in the group with high Ki-67 expression. For predicting high Ki-67 expression, the AUC values were 0.701 for mean ADC, 0.818 for minimum ADC, and 0.571 for maximum ADC. The diagnostic sensitivity and specificity of the minimum ADC values were 88.9% and 76.2%, respectively. In addition, with ADC values combined, the AUC increased to 0.831. Apparent diffusion coefficient is a useful predictor of Ki-67 expression level in ICC. Apparent diffusion coefficient values, especially minimum ADC values, can noninvasively predict ICC associated with high Ki-67 expression.

Structure-changeable luminescent Eu(III) complex as a human cancer grade probing system for brain tumor diagnosis.

Accurate determination of human tumor malignancy is important for choosing efficient and safe therapies. Bioimaging technologies based on luminescent molecules are widely used to localize and distinguish active tumor cells. Here, we report a human cancer grade probing system (GPS) using a water-soluble and structure-changeable Eu(III) complex for the continuous detection of early human brain tumors of different malignancy grades. Time-dependent emission spectra of the Eu(III) complexes in various types of tumor cells were recorded. The radiative rate constants (kr), which depend on the geometry of the Eu(III) complex, were calculated from the emission spectra. The tendency of the kr values to vary depended on the tumor cells at different malignancy grades. Between T = 0 and T = 3h of invasion, the kr values exhibited an increase of 4% in NHA/TS (benign grade II gliomas), 7% in NHA/TSR (malignant grade III gliomas), and 27% in NHA/TSRA (malignant grade IV gliomas). Tumor cells with high-grade malignancy exhibited a rapid upward trend in kr values. The cancer GPS employs Eu(III) emissions to provide a new diagnostic method for determining human brain tumor malignancy.

Identification of specific codon 201 mutation of the DCC Gene in the colonoscopic specimen of colorectal cancer.

To identify the mutation in codon 201 of the deleted in colorectal cancer gene in colorectal cancer, and to correlate that mutation to the histopathological grading of colorectal cancer. The cross-sectional study was conducted from February 2019 to February 2021 after approval from the ethics review board of the Dow University of Health Sciences, Karachi, and comprised biopsy-proven colorectal cancer patients regardless of age and gender. After histopathological reporting, formalin-fixed paraffin-embedded tissue blocks of colorectal cancer were used for deoxyribonucleic acid extraction, followed by polymerase chain reaction optimisation and deoxyribonucleic acid Sanger sequencing for mutational analysis. Data was analysed using SPSS 25. Of the 100 biopsy specimens assessed, 45(45%) were selected. Of them, 13(29%) samples failed to show any band on gel electrophoresis. The remaining 32(71%) samples were used for Sanger sequencing. Of these, 1(3%) sample did not sequence, while 31(97%) showed sequencing. All the sequenced samples identified a mutation in codon 201 of exon 3 in the deleted in colorectal cancer gene; 30(97%) showed homozygosity, and 1(3%) showed heterozygosity. No significant association of point mutation was noted with various demographic and clinicopathological parameters (p>0.05). The deleted in colorectal cancer gene's missense mutation in codon 201 was frequently observed in colorectal cancer patients.

A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study.

The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Artemis as Predictive Biomarker of Responsiveness to Preoperative Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer.

The aim of this study was to identify Artemis as a predictive biomarker for guiding preoperative chemoradiotherapy in locally advanced rectal cancer. The resection specimens were collected from 50 patients with rectal cancer who underwent preoperative chemoradiotherapy. Artemis expression in biopsy tissues was evaluated using immunohistochemical staining according to the percentage of positively stained cells combined with staining intensity. Among the 50 patients, 36 (72%) had a weakly positive Artemis protein expression, 10 (20%) had a moderately positive expression, and 4 (8%) showed a strongly positive expression. The criteria of magnetic resonance imaging tumor regression grade (mrTRG) and pathological rectal cancer regression grade (RCRG) were used to assess the tumor response to chemoradiotherapy. Correlation analysis shows that there is a significant negative correlation between high Artemis immunoscore and treatment response (r = -0.532, p < 0.001). The results imply that high Artemis expression was associated with poor treatment response. Our study suggested a potential role of Artemis as a predictive biomarker of the tumor response to preoperative chemoradiotherapy in patients with locally advanced rectal cancer.