Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis.

BACKGROUND: Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology. METHODS: The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted. RESULTS: The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037). CONCLUSIONS: We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

An MRI-based scoring system for pretreatment risk stratification in locally advanced rectal cancer.

BACKGROUND: Accurately assessing the risk of recurrence in patients with locally advanced rectal cancer (LARC) before treatment is important for the development of treatment strategies. The purpose of this study is to develop an MRI-based scoring system to predict the risk of recurrence in patients with LARC. METHODS: This was a multicenter observational study that enrolled participants who underwent neoadjuvant chemoradiotherapy. To evaluate the risk of recurrence in these patients, we developed the mrDEC scoring system and assessed inter-reader agreement. Additionally, we plotted Kaplan-Meier curves to compare the 3-year disease-free survival (DFS) and 5-year overall survival (OS) rates among patients with different mrDEC scores. RESULTS: A total of 1287 patients with LARC were included in this study. We observed substantial inter-reader agreement for mrDEC. Based on the mrDEC scores ranging from 0 to 3, the patients were categorized into four groups. The 3-year DFS rates for the groups were 91.0%, 79.5%, 65.5%, and 44.0% (P < 0.0001), respectively, and the 5-year OS rates were 92.9%, 87.1%, 74.8%, and 44.5%, respectively (P < 0.0001). CONCLUSIONS: The mrDEC scoring system proved to be an effective tool for predicting the prognosis of patients with LARC and can assist clinicians in clinical decision-making.

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer.

BACKGROUND: We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer. METHODS: A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3+ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival. RESULT: Patients were classified into 4-level score groups (score 1-4). A high Deep-immune score was associated with a high level of CD3+ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15-0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15-0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018). CONCLUSION: The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.

Cutoff points of T1 rho/T2 mapping relaxation times distinguishing early-stage and advanced osteoarthritis.

Introduction: The histopathology grading system is the gold standard post-operative method to evaluate cartilage degeneration in knee osteoarthritis (OA). Magnetic resonance imaging (MRI) T1 rho/T2 mapping imaging can be used for preoperative detection. An association between histopathology and T1 rho/T2 mapping relaxation times was suggested in previous research. However, the cutoff point was not determined among different histopathology grades. Our study aimed to determine the cutoff point of T1 rho/T2 mapping. Material and methods: T1 rho/T2 mapping images were acquired from 80 samples before total knee replacements. Then the histopathology grading system was applied. Results: The mean T1 rho/T2 mapping relaxation times of 80 samples were 39.17 ms and 37.98 ms respectively. Significant differences were found in T1 rho/T2 mapping values between early-stage and advanced OA (p < 0.001). The cutoff point for T1 rho was 33 ms with a sensitivity of 94.12 (95% CI: 80-99.3) and a specificity of 91.30 (95% CI: 79.2-97.6). The cutoff point for T2 mapping was suggested as 35.04 ms with a sensitivity of 88.24 (95% CI: 72.5-96.7) and specificity of 97.83 (95% CI: 88.5-99.9). After bootstrap simulation, the 95% CI of the T1 rho/T2 mapping cutoff point was estimated as 29.36 to 36.32 ms and 34.8 to 35.04 ms respectively. The area under the PR curve of T1 rho/T2 mapping was 0.972 (95% CI: 0.925-0.992) and 0.949 (95% CI: 0.877-0.989) respectively. Conclusions: The cutoff point of T1 rho relaxation times, which was suggested as 33 ms, could be used to distinguish early-stage and advanced OA.

Combining Magnetic Resonance Diffusion-Weighted Imaging with Prostate-Specific Antigen to Differentiate Between Malignant and Benign Prostate Lesions.

BACKGROUND We aimed to develop a combined model of quantitative parameters derived from 3 different magnetic resonance imaging (MRI) diffusion models and laboratory data related to prostate-specific antigen (PSA) for differentiating between prostate cancer (PCa) and benign lesions. MATERIAL AND METHODS Eighty-four patients pathologically confirmed as having PCa or benign disease were enrolled. All patients underwent multiparametric MRI before biopsy, added intravoxel incoherent motion (IVIM) imaging, and diffusion kurtosis imaging (DKI). The following data were collected: quantitative parameters of diffusion-weighted imaging (DWI), IVIM, and DKI, preoperative total PSA, free/total PSA ratio, and PSA density (PSAD) values. A combined logistic regression model was established by above MRI quantitative parameters and PSA data to diagnose PCa. The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was used to assess the lesions for comparison. RESULTS Thirty-two patients had PCa and 52 patients had benign lesions. In multivariate logistic regression analysis, only apparent diffusion coefficient (ADC) and PSAD were significant variables (P<0.05) and were thus retained in the model. The area under curve value of the combined model (0.911) was higher than that of ADC, PSAD, and PI-RADS v2 (0.887, 0.861, and 0.859, respectively) in univariate analysis, but without any statistically significant differences. The combined model generated greater clinical benefit than the independent application of ADC, PSAD, and PI-RADS v2. CONCLUSIONS ADC and PSAD were the 2 most important metrics for distinguishing PCa from benign lesions. The combined model of ADC and PSAD demonstrated satisfactory discrimination and improved clinical net benefit.

The Crohn's-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer.

BACKGROUND: The Crohn's-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification. METHODS: The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N = 279) and a validation (N = 194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS). RESULT: The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38-0.89, P = 0.012) and validation cohort (0.45, 0.23-0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability. CONCLUSION: We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists' workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value.

Predicting Neoadjuvant Chemoradiotherapy Response in Locally Advanced Rectal Cancer Using Tumor-Infiltrating Lymphocytes Density.

PURPOSE: Accumulating evidence revealed the predictive value of tumor-infiltrating lymphocytes (TILs) for neoadjuvant chemoradiotherapy (nCRT) response in solid tumors. This study quantified TILs density using hematoxylin and eosin (H&E) stained whole-slide images (WSIs) and investigated the predictive value of TILs density on nCRT response in locally advanced rectal cancer (LARC) patients. PATIENTS AND METHODS: Two hundred and ten patients diagnosed with LARC were enrolled in this study. The density of TILs in the stroma region was quantified by a semi-automatic method in WSIs. Patients were stratified into low-TILs and high-TILs groups using the median value as the threshold. The tumor regression grade (TRG) was used to assess the response to nCRT in tumor resected specimens. Based on TRG, patients were classified into major-responder (TRG 0-1) and non-responder (TRG 2-3) groups. RESULTS: The TILs density was significantly correlated with the nCRT response. Specifically, patients with high-TILs tend to have a higher major-responder rate than the low-TILs group (63.8% vs 47.6%, P = 0.026). Univariate analysis showed the TILs density was a predictor for the nCRT response (high vs low, odds ratio [OR] =1.94, 95% confidence interval 1.12-3.37, P = 0.019), and multivariate analysis further confirmed the correlation (adjusted odds ratio [AOR] = 2.41, 1.28-4.56, P = 0.007). CONCLUSION: Patients with a high-TIL density have a higher major-responder rate than the low-TILs group, indicating patients with a strong immune response benefit more from nCRT. This semi-automatic method can facilitate the individualized preoperative prediction of the TRG for LARC patients before nCRT.

Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images.

Background: In colorectal cancer (CRC), mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype, morphology, and prognosis. However, mucinous components are present in a large number of adenocarcinomas, and the prognostic value of mucus proportion has not been investigated. Artificial intelligence provides a way to quantify mucus proportion on whole-slide images (WSIs) accurately. We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts. Methods: Deep learning was used to segment WSIs stained with hematoxylin and eosin. Mucus-tumor ratio (MTR) was defined as the proportion of mucinous component in the tumor area. A training cohort (N = 419) and a validation cohort (N = 315) were used to evaluate the prognostic value of MTR. Survival analysis was performed using the Cox proportional hazard model. Result: Patients were stratified to mucus-low and mucus-high groups, with 24.1% as the threshold. In the training cohort, patients with mucus-high had unfavorable outcomes (hazard ratio for high vs. low 1.88, 95% confidence interval 1.18-2.99, P = 0.008), with 5-year overall survival rates of 54.8% and 73.7% in mucus-high and mucus-low groups, respectively. The results were confirmed in the validation cohort (2.09, 1.21-3.60, 0.008; 62.8% vs. 79.8%). The prognostic value of MTR was maintained in multivariate analysis for both cohorts. Conclusion: The deep learning quantified MTR was an independent prognostic factor in CRC. With the advantages of advanced efficiency and high consistency, our method is suitable for clinical application and promotes precision medicine development.

Histological Grade and Magnetic Resonance Imaging Quantitative T1rho/T2 Mapping in Osteoarthritis of the Knee: A Study in 20 Patients.

BACKGROUND Magnetic resonance imaging (MRI) of osteoarthritis (OA) of the knee is a preoperative method of joint assessment. Histology of the joint is invasive and performed after surgery. T1rho/T2 MRI mapping is a new preoperative method of quantifying joint changes. This study aimed to analyze and compare the histological changes in the joint cartilage with the use of quantitative T1rho/T2 MRI mapping in patients with OA of the knee. MATERIAL AND METHODS Twenty patients with OA of the knee (20 knees) underwent preoperative MRI with T1rho mapping, T2 mapping, T1-weighted, and T2-weighted fat-suppressed MRI sequences. The degree of OA of the knee on MRI was graded according to the Osteoarthritis Research Society International (OARSI) criteria and the Kellgren-Lawrence grading system. Histological grading of OA used the OARSI criteria. Four tibiofemoral condyles were assessed histologically, and the degree of cartilage destruction was determined using the OARSI criteria. Two investigators performed cartilage segmentation for T1rho/T2 values. RESULTS Histology of the four knee joint condyles confirmed mild to severe OA. The histology of the cartilage thickness (P<0.001) and the MRI findings of the distal medial condyle (P<0.00) were significantly different from the other three knee joint condyles. The T2 and T1rho values of each condyle were significantly correlated with the histological grade (II-IV) of the joint condyles, including the cartilage volume, cartilage defects, thickness, and bone lesions (P<0.05). CONCLUSIONS In 20 patients with OA of the knee, preoperative T2/T1rho MRI identified Grade II-IV OA changes in the joint.