Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma.

PURPOSE: This study was designed to investigate the prognostic significance of artificial intelligence (AI)-based quantification of myxoid stroma in patients undergoing esophageal squamous cell carcinoma (ESCC) surgery after neoadjuvant chemotherapy (NAC) and to verify its significance in an independent validation cohort from another hospital. METHODS: We evaluated two datasets of patients with pathological stage II or III ESCC who underwent surgery after NAC. Cohort 1 consisted of 85 patients who underwent R0 surgery for the primary tumor after NAC. Cohort 2, the validation cohort, consisted of 80 patients who received same treatments in another hospital. AI-based myxoid stroma was evaluated in resected specimens, and its area was categorized by using the receiver operating characteristic curve for overall survival (OS) of cohort 1. RESULTS: The F1 scores, which are the degree of agreement between the automatically detected myxoid stroma and manual annotations, were 0.83 and 0.79 for cohorts 1 and 2. The myxoid stroma-high group had a significantly poorer prognosis than the myxoid stroma-low group in terms of OS, disease-specific survival (DSS), and recurrence-free survival (RFS) in cohort 1. Comparable results were observed in cohort 2, where OS, DSS, and RFS were significantly affected by myxoid stroma. Multivariate analysis for RFS revealed that AI-determined myxoid stroma-high was one of the independent prognostic factors in cohort 1 (hazard ratio [HR] 1.97, p = 0.037) and cohort 2 (HR 4.45, p < 0.001). CONCLUSIONS: AI-determined myxoid stroma may be a novel and useful prognostic factor for patients with pathological stage II or III ESCC after NAC.

Cancer-associated fibroblasts at the unfavorable desmoplastic stroma promote colorectal cancer aggressiveness: Potential role of ADAM9.

The tumor microenvironment plays a key role in cancer aggressiveness. Desmoplastic reaction (DR), morphologically classified as Mature, Intermediate and Immature types, has previously been shown to be highly prognostic in colorectal cancer (CRC) and it consists to a large extent of cancer-associated fibroblasts (CAFs). The aim of our study was to characterize the molecular background of DR and understand the effects of CAFs in tumor aggressiveness. The prognostic significance of DR was initially examined in 1497 patients. Then CAFs originating from patient tissues with different DR types were isolated and their impact on tumor growth was examined both in vitro and in vivo. DR was shown to be highly prognostic, with patients within the Immature DR group conferring the worst relapse-free survival. The conditioned media of CAFs from tumor with Immature-type DR (CAFsImmature ) significantly increased proliferation and migration of CRC cell lines and growth of CRC-derived organoids compared to that of CAFs from Mature-type DR (CAFsMature ). Subcutaneous or orthotopic implantation of CRC cells together with CAFsImmature in mice significantly promoted tumor growth and dissemination compared to implantation with CAFsMature . Systematic examination of the expression of "a disintegrin and metalloproteinases" (ADAMs) in CAFs isolated from CRC tissues showed that the secreted isoform of ADAM9 (ADAM9s) was significantly higher in CAFsImmature than in CAFsMature . Knockdown of ADAM9s in CAFsImmature abrogated the promoting effects on CRC cell proliferation and migration. CAFs-derived ADAM9s is implicated in deteriorating survival in CRC patients with Immature-type DR by increasing tumor cell proliferation and dissemination.

Deep-learning-based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma.

AIMS: Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study aimed to investigate whether a deep-learning classifier could be used for DR classification. We further assessed the prognostic significance of the deep-learning classifier and compared it to that of manual DR reporting and other pathological factors currently used in the clinic. METHODS AND RESULTS: From 222 surgically resected ESCC cases, 31 randomly selected haematoxylin-eosin-digitised whole slides of patients with immature DR were used to train and develop a deep-learning classifier. The classifier was trained for 89 370 iterations. The accuracy of the deep-learning classifier was assessed to 30 unseen cases, and the results revealed a Dice coefficient score of 0.81. For survival analysis, the classifier was then applied to the entire cohort of patients, which was split into a training (n = 156) and a test (n = 66) cohort. The automated DR classification had a higher prognostic significance for disease-specific survival than the manually classified DR in both the training and test cohorts. In addition, the automated DR classification outperformed the prognostic accuracy of the gold-standard factors of tumour depth and lymph node metastasis. CONCLUSIONS: This study demonstrated that DR can be objectively and quantitatively assessed in ESCC using a deep-learning classifier and that automatically classed DR has a higher prognostic significance than manual DR and other features currently used in the clinic.

Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning.

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier's performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.

The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10-5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.

Prognostic significance of mesothelin expression in colorectal cancer disclosed by area-specific four-point tissue microarrays.

Mesothelin (MSLN) is a cell surface glycoprotein present in many cancer types. Its expression is generally associated with an unfavorable prognosis. This study examined the prognostic significance of MSLN expression in different areas of individual colorectal cancers (CRCs) using tissue microarrays (TMAs) by enrolling 314 patients with stage II (T3-T4, N0, M0) CRCs. Using formalin-fixed paraffin-embedded tissue blocks from patients, TMA blocks were constructed. Tissue core specimens were obtained from submucosal invasive front (Fr-sm), subserosal invasive front (Fr-ss), central area (Ce), and rolled edge (Ro) of each tumor. Using these four-point TMA sets, MSLN expression was immunohistochemically surveyed. The area-specific prognostic significance of MSLN expression was evaluated. A deep learning convolutional neural network algorithm was used for imaging analysis and evaluating our judgment's objectivity. MSLN staining ratio was positively correlated between the manual and machine-learning analyses (r = 0.71). The correlation coefficient between Ro and Ce, Ro and Fr-sm, and Ro and Fr-ss was r = 0.63, r = 0.54, and r = 0.61, respectively. Disease-specific survival curves for the MSLN-positive and MSLN-negative groups in Fr-sm, Fr-ss, and Ro were significantly different (five-year survival rates 88.1% and 95.5% (P = 0.024), 85.0 and 96.2% (P = 0.0087), 87.8 and 95.5% (P = 0.051), and 77.9 and 95.8% (P = 0.046) for Fr-sm, Fr-ss, Ce, and Ro, respectively). The analysis performed using area-specific four-point TMAs clearly demonstrated that MSLN expression in stage II CRC was relatively homogeneous within tumors. Additionally, high MSLN expression showed or tended to show unfavorable prognostic significance regardless of the tumor area.

Spatial immune profiling of the colorectal tumor microenvironment predicts good outcome in stage II patients.

Cellular subpopulations within the colorectal tumor microenvironment (TME) include CD3+ and CD8+ lymphocytes, CD68+ and CD163+ macrophages, and tumor buds (TBs), all of which have known prognostic significance in stage II colorectal cancer. However, the prognostic relevance of their spatial interactions remains unknown. Here, by applying automated image analysis and machine learning approaches, we evaluate the prognostic significance of these cellular subpopulations and their spatial interactions. Resultant data, from a training cohort retrospectively collated from Edinburgh, UK hospitals (n = 113), were used to create a combinatorial prognostic model, which identified a subpopulation of patients who exhibit 100% survival over a 5-year follow-up period. The combinatorial model integrated lymphocytic infiltration, the number of lymphocytes within 50-µm proximity to TBs, and the CD68+/CD163+ macrophage ratio. This finding was confirmed on an independent validation cohort, which included patients treated in Japan and Scotland (n = 117). This work shows that by analyzing multiple cellular subpopulations from the complex TME, it is possible to identify patients for whom surgical resection alone may be curative.

Automated Analysis of Lymphocytic Infiltration, Tumor Budding, and Their Spatial Relationship Improves Prognostic Accuracy in Colorectal Cancer.

Both immune profiling and tumor budding significantly correlate with colorectal cancer patient outcome but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II colorectal cancer. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3+CD8+ T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: n = 114, validation cohort 1: n = 56, validation cohort 2: n = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs [HR = 5.899; 95% confidence interval (CI) 1.875-18.55], low CD3+ T-cell density (HR = 9.964; 95% CI, 3.156-31.46), and low mean number of CD3+CD8+ T cells within 50 µm of TBs (HR = 8.907; 95% CI, 2.834-28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75; 95% CI, 6.46-54.43), than TBs, Immunoscore, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27; 95% CI, 3.524-42.73; HR = 15.61; 95% CI, 4.692-51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II colorectal cancer through an automated image analysis and machine learning workflow.

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.

Novel Internationally Verified Method Reports Desmoplastic Reaction as the Most Significant Prognostic Feature For Disease-specific Survival in Stage II Colorectal Cancer.

Multiple histopathologic features have been reported as candidates for predicting aggressive stage II colorectal cancer (CRC). These include tumor budding (TB), poorly differentiated clusters (PDC), Crohn-like lymphoid reaction and desmoplastic reaction (DR) categorization. Although their individual prognostic significance has been established, their association with disease-specific survival (DSS) has not been compared in stage II CRC. This study aimed to evaluate and compare the prognostic value of the above features in a Japanese (n=283) and a Scottish (n=163) cohort, as well as to compare 2 different reporting methodologies: analyzing each feature from across every tissue slide from the whole tumor and a more efficient methodology reporting each feature from a single slide containing the deepest tumor invasion. In the Japanese cohort, there was an excellent agreement between the multi-slide and single-slide methodologies for TB, PDC, and DR (κ=0.798 to 0.898) and a good agreement when assessing Crohn-like lymphoid reaction (κ=0.616). TB (hazard ratio [HR]=1.773; P=0.016), PDC (HR=1.706; P=0.028), and DR (HR=2.982; P<0.001) based on the single-slide method were all significantly associated with DSS. DR was the only candidate feature reported to be a significant independent prognostic factor (HR=2.982; P<0.001) with both multi-slide and single-slide methods. The single-slide result was verified in the Scottish cohort, where multivariate Cox regression analysis reported that DR was the only significant independent feature (HR=1.778; P=0.002) associated with DSS. DR was shown to be the most significant of all the analyzed histopathologic features to predict disease-specific death in stage II CRC. We further show that analyzing the features from a single-slide containing the tumor's deepest invasion is an efficient and quicker method of evaluation.