Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer

As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication.Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis. Both manual and automated scores were correlated to clinicopathological and survival data.E-cadherin data generated through digital image analysis was superior to manual evaluation when investigating for clinicopathological correlations in colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = <0.001), higher T-stage (p = <0.001), higher grade (p = <0.001), N2 nodal stage (p = <0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) survival status. Manual assessment was only correlated with higher grade tumours, though other correlations become apparent only when assessed for morphological expression pattern (circumferential, basolateral, parallel) irrespective of intensity. Digital assessment of E-cadherin is effective for prognostication of colorectal cancer and may potentially offer benefits of improved objectivity, accuracy, and economy of time. Incorporating tools to assess patterns of staining may further improve such digital assessment in the future

Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma

Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses

Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://github.com/hrzhang1123/CVM-WS-Segmentation

Weakly Supervised Segmentation with Point Annotations for Histopathology Images via Contrast-Based Variational Model

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://github.com/hrzhang1123/CVM-WS-Segmentation

Associations between AI-assisted tumor amphiregulin and epiregulin IHC and outcomes from anti-EGFR therapy in the routine management of metastatic colorectal cancer.

PurposeHigh tumor production of the EGFR ligands, amphiregulin (AREG) and epiregulin (EREG), predicted benefit from anti-EGFR therapy for metastatic colorectal cancer (mCRC) in a retrospective analysis of clinical trial data. Here, AREG/EREG immunohistochemistry (IHC) was analyzed in a cohort of patients who received anti-EGFR therapy as part of routine care, including key clinical contexts not investigated in the previous analysis.Experimental designPatients who received panitumumab or cetuximab ± chemotherapy for treatment of RAS wild-type mCRC at eight UK cancer centers were eligible. Archival FFPE tumor tissue was analyzed for AREG and EREG IHC in six regional laboratories using previously developed artificial intelligence technologies. Primary endpoints were progression-free survival (PFS) and overall survival (OS).Results494 of 541 patients (91.3%) had adequate tissue for analysis. 45 were excluded after central extended RAS testing, leaving 449 patients in the primary analysis population. After adjustment for additional prognostic factors, high AREG/EREG expression (n=360; 80.2%) was associated with significantly prolonged PFS (median: 8.5 vs 4.4 months; HR 0.73; 95% CI, 0.56-0.95; p=0.02) and OS (median: 16.4 vs 8.9 months; HR 0.66 [0.50-0.86]; p=0.002). The significant OS benefit was maintained among patients with right primary tumor location (PTL), those receiving cetuximab or panitumumab, those with an oxaliplatin- or irinotecan-based chemotherapy backbone, and those with tumor tissue obtained by biopsy or surgical resection.ConclusionsHigh tumor AREG/EREG expression was associated with superior survival outcomes from anti-EGFR therapy in mCRC, including in right PTL disease. AREG/EREG IHC assessment could aid therapeutic decisions in routine practice

Associations between AI-assisted tumor amphiregulin and epiregulin IHC and outcomes from anti-EGFR therapy in the routine management of metastatic colorectal cancer.

High tumor production of the EGFR ligands, amphiregulin (AREG) and epiregulin (EREG), predicted benefit from anti-EGFR therapy for metastatic colorectal cancer (mCRC) in a retrospective analysis of clinical trial data. Here, AREG/EREG immunohistochemistry (IHC) was analyzed in a cohort of patients who received anti-EGFR therapy as part of routine care, including key clinical contexts not investigated in the previous analysis. Patients who received panitumumab or cetuximab ± chemotherapy for treatment of RAS wild-type mCRC at eight UK cancer centers were eligible. Archival FFPE tumor tissue was analyzed for AREG and EREG IHC in six regional laboratories using previously developed artificial intelligence technologies. Primary endpoints were progression-free survival (PFS) and overall survival (OS). 494 of 541 patients (91.3%) had adequate tissue for analysis. 45 were excluded after central extended RAS testing, leaving 449 patients in the primary analysis population. After adjustment for additional prognostic factors, high AREG/EREG expression (n=360; 80.2%) was associated with significantly prolonged PFS (median: 8.5 vs 4.4 months; HR 0.73; 95% CI, 0.56-0.95; p=0.02) and OS (median: 16.4 vs 8.9 months; HR 0.66 [0.50-0.86]; p=0.002). The significant OS benefit was maintained among patients with right primary tumor location (PTL), those receiving cetuximab or panitumumab, those with an oxaliplatin- or irinotecan-based chemotherapy backbone, and those with tumor tissue obtained by biopsy or surgical resection. High tumor AREG/EREG expression was associated with superior survival outcomes from anti-EGFR therapy in mCRC, including in right PTL disease. AREG/EREG IHC assessment could aid therapeutic decisions in routine practice

Supplementary Table S1 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Primer details</p

Supplementary Figure S3 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Scatterplot: AREG vs EREG</p

Supplementary Methods S1 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Supplementary methods</p

Supplementary Table S3 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Representativeness of study participants</p