Interobserver variability in assessing dysplasia and architecture in colorectal adenomas: a multicentre Canadian study

Sporadic Polyps of the Colorectum

Colorectal polyps are common, and their diagnosis and classification represent a major component of gastrointestinal pathology practice. The majority of colorectal polyps represent precursors of either the chromosomal instability or serrated neoplasia pathways to colorectal carcinoma. Accurate reporting of these polyps has major implications for surveillance and thus for cancer prevention. In this review, we discuss the key histologic features of the major colorectal polyps with a particular emphasis on diagnostic pitfalls and areas of contention.

Accuracy of GPT-4 in histopathological image detection and classification of colorectal adenomas

AIMS

To evaluate the accuracy of Chat Generative Pre-trained Transformer (ChatGPT) powered by GPT-4 in histopathological image detection and classification of colorectal adenomas using the diagnostic consensus provided by pathologists as a reference standard.

METHODS

A study was conducted with 100 colorectal polyp photomicrographs, comprising an equal number of adenomas and non-adenomas, classified by two pathologists. These images were analysed by classic GPT-4 for 1 time in October 2023 and custom GPT-4 for 20 times in December 2023. GPT-4's responses were compared against the reference standard through statistical measures to evaluate its proficiency in histopathological diagnosis, with the pathologists further assessing the model's descriptive accuracy.

RESULTS

GPT-4 demonstrated a median sensitivity of 74% and specificity of 36% for adenoma detection. The median accuracy of polyp classification varied, ranging from 16% for non-specific changes to 36% for tubular adenomas. Its diagnostic consistency, indicated by low kappa values ranging from 0.06 to 0.11, suggested only poor to slight agreement. All of the microscopic descriptions corresponded with their diagnoses. GPT-4 also commented about the limitations in its diagnoses (eg, slide diagnosis best done by pathologists, the inadequacy of single-image diagnostic conclusions, the need for clinical data and a higher magnification view).

CONCLUSIONS

GPT-4 showed high sensitivity but low specificity in detecting adenomas and varied accuracy for polyp classification. However, its diagnostic consistency was low. This artificial intelligence tool acknowledged its diagnostic limitations, emphasising the need for a pathologist's expertise and additional clinical context.

Laboratory variation in the grading of dysplasia of duodenal adenomas in familial adenomatous polyposis patients

To prevent duodenal and ampullary cancer in familial adenomatous polyposis (FAP) patients, a diagnosis of high grade dysplasia (HGD) plays an important role in the clinical management. Previous research showed that FAP patients are both over- and undertreated after a misdiagnosis of HGD, indicating unwarranted variation. We aimed to investigate the laboratory variation in dysplasia grading of duodenal adenomas and explore possible explanations for this variation. We included data from all Dutch pathology laboratories between 1991 and 2020 by retrieving histology reports from upper endoscopy specimens of FAP patients from the Dutch nationwide pathology databank (PALGA). Laboratory variation was investigated by comparing standardized proportions of HGD. To describe the degree of variation between the laboratories a factor score was calculated. A funnel plot was used to identify outliers. A total of 3050 specimens from 25 laboratories were included in the final analyses. The mean observed HGD proportion was 9.4%. The top three HGD-diagnosing laboratories diagnosed HGD 3.9 times more often than the lowest three laboratories, even after correcting for case-mix. No outliers were identified. Moderate laboratory variation was found in HGD diagnoses of duodenal tissue of FAP patients after adjusting for case-mix. Despite the fact that no outliers were observed, there may well be room for quality improvement. Concentration of these patients in expertise centers may decrease variation. To further reduce unwarranted variation, we recommend (inter)national guidelines to become more uniform in their recommendations regarding duodenal tissue sampling and consequences of HGD diagnoses.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

Predicting the Likelihood of Colorectal Cancer with Artificial Intelligence Tools Using Fourier Transform Infrared Signals Obtained from Tumor Samples

The early and accurate detection of colorectal cancer (CRC) significantly affects its prognosis and clinical management. However, current standard diagnostic procedures for CRC often lack sensitivity and specificity since most rely on visual examination. Hence, there is a need to develop more accurate methods for its diagnosis. Support vector machine (SVM) and feedforward neural network (FNN) models were designed using the Fourier transform infrared (FT-IR) spectral data of several colorectal tissues that were unanimously identified as either benign or malignant by different unrelated pathologists. The set of samples in which the pathologists had discordant readings were then analyzed using the AI models described above. Between the SVM and NN models, the NN model was able to outperform the SVM model based on their prediction confidence scores. Using the spectral data of the concordant samples as training set, the FNN was able to predict the histologically diagnosed malignant tissues (n = 118) at 59.9-99.9% confidence (average = 93.5%). Of the 118 samples, 84 (71.18%) were classified with an above average confidence score, 34 (28.81%) classified below the average confidence score, and none was misclassified. Moreover, it was able to correctly identify the histologically confirmed benign samples (n = 83) at 51.5-99.7% confidence (average = 91.64%). Of the 83 samples, 60 (72.29%) were classified with an above average confidence score, 22 (26.51%) classified below the average confidence score, and only 1 sample (1.20%) was misclassified. The study provides additional proof of the ability of attenuated total reflection (ATR) FT-IR enhanced by AI tools to predict the likelihood of CRC without dependence on morphological changes in tissues.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

Discrepancy Between Forceps Biopsy and Resection in Colorectal Polyps: A 1686 Paired Screening-Therapeutic Colonoscopic Finding

Purpose

To identify pathology discrepancy between forceps biopsies and polypectomy specimens in colorectal polyps, as well as the reliability of biopsy-based treatment strategy.

Methods

All endoscopic polypectomy cases with forceps biopsies performed within 6 months were included in the study. The biopsies were compared with polypectomy specimens in terms of concordance of histological diagnosis. A logistic regression model was used to investigate the independent predictors of upgrade in histological diagnosis compared with concordance in histological diagnosis.

Results

A total of 1686 paired screening-therapeutic colonoscopies and 1739 paired biopsy-polypectomy specimens were enrolled in the study. The grade of dysplasia in 84.5% of biopsy specimens were concordant to polypectomy specimens, but this proportion decreased to 75.4% when the specimens were classified using tubular or villousness structure. 10.1% and 5.4% of biopsy specimens were upgraded and downgraded in assessing grade of dysplasia, respectively, while 14.3% and 10.3% of biopsy specimens were upgraded and downgraded in assessing tubular or villousness structure, respectively. In subgroup analysis stratified by size of polyps, 9.0% and 10.6% of biopsies obtained from polyps smaller than 10 mm were upgraded in assessing dysplasia and tubular or villousness structure, respectively. This proportion increased to 10.7% and 21.3%, respectively, in biopsies obtained from polyps larger than 10 mm. Larger size of polyps and pedunculated polyps were associated with a higher incidence of upgrade in histological diagnosis. Nearly 25% of biopsy specimens with high-grade dysplasia were identified as adenocarcinoma in polypectomy specimens.

Conclusion

The concordance between biopsy and polypectomy specimens is not adequate. The biopsy-based treatment strategy is not reliable and should not be considered as an indicator for further treatment, particularly in large or pedunculated polyps.

Evaluation of the polyp-based resect and discard strategy: a retrospective study

BACKGROUND

Standard colonoscopy practice requires removal and histological characterization of almost all detected small (< 10 mm) and diminutive (≤ 5 mm) colorectal polyps. This study aimed to test a simplified polyp-based resect and discard (PBRD) strategy that assigns surveillance intervals based only on size and number of small/diminutive polyps, without the need for pathology examination.

METHODS

A post hoc analysis was performed on patients enrolled in a prospective study. The primary outcome was surveillance interval agreement of the PBRD strategy with pathology-based management according to 2020 US Multi-Society Task Force guidelines. Chart analysis also evaluated clinician adherence to pathology-based recommendations. One-sided testing was performed with a null-hypothesis of 90 % agreement with pathology-based surveillance intervals and a two-sided 96.7 % confidence interval (CI) using correction for multiple testing.

RESULTS

452 patients were included in the study. Surveillance intervals assigned using the PBRD strategy were correct in 97.8 % (96.7 %CI 96.3-99.3 %) of patients compared with pathology-based management. The PBRD strategy reduced pathology examinations by 58.7 % while providing 87.8 % of patients with immediate surveillance interval recommendations on the day of colonoscopy, compared with 47.1 % when using pathology-based management. Chart analysis of surveillance interval assignments showed 63.3 % adherence to pathology-based guidelines.

CONCLUSION

The PBRD strategy surpassed the 90 % agreement with the pathology-based standard for determining surveillance interval, reduced the need for pathology examinations, and increased the proportion of patients receiving immediate surveillance interval recommendations. The PBRD strategy does not require expertise in optical diagnosis and may replace histological characterization of small and diminutive colorectal polyps.

Diagnostic variability in the histopathological assessment of advanced colorectal adenomas and early colorectal cancer in a screening population

Aim

The aim of this study was to evaluate interobserver variability between individual pathologists and a panel of pathologists in the histopathological assessment of advanced colorectal neoplasms in the Dutch bowel cancer screening population.

Methods and results

Histological slides of adenomas with high‐grade dysplasia and early colorectal carcinomas (CRC) from 20 different laboratories were reviewed by the pathology panel of the Dutch bowel screening programme. Interobserver variability was reported by descriptive statistics. In addition, potential clinical consequences of discrepancies were evaluated. A total of 104 cases of adenomas with high‐grade dysplasia and 83 early CRCs were reviewed. Discrepancies were observed in 41 of 104 (39.4%) adenoma cases, which potentially had clinical consequences in 16 (15.4%) cases. For CRC, discrepancies were shown in 44 of 83 cases (53.0%) and would have potentially led to alternative treatment strategies in 25 (30.1%) cases. Most frequently, discrepancies were observed in the assessment of lymphovascular invasion (23 of 73 cases, 31.5%).

Conclusion

This study showed that considerable interobserver variability is present in the histopathological assessment of advanced colorectal neoplasia, which may impact upon treatment choices. Additional stains and education, as well as intercollegial consultation, might decrease this variability.

Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps

Importance

Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists' classification of colorectal polyps, and improved assessment by pathologists may be associated with reduced subsequent underuse and overuse of colonoscopy.

Objective

To compare standard microscopic assessment with an artificial intelligence (AI)-augmented digital system that annotates regions of interest within digitized polyp tissue and predicts polyp type using a deep learning model to assist pathologists in colorectal polyp classification.

Design, Setting, and Participants

In this diagnostic study conducted at a tertiary academic medical center and a community hospital in New Hampshire, 100 slides with colorectal polyp samples were read by 15 pathologists using a microscope and an AI-augmented digital system, with a washout period of at least 12 weeks between use of each modality. The study was conducted from February 10 to July 10, 2020.

Main Outcomes and Measures

Accuracy and time of evaluation were used to compare pathologists' performance when a microscope was used with their performance when the AI-augmented digital system was used. Outcomes were compared using paired t tests and mixed-effects models.

Results

In assessments of 100 slides with colorectal polyp specimens, use of the AI-augmented digital system significantly improved pathologists' classification accuracy compared with microscopic assessment from 73.9% (95% CI, 71.7%-76.2%) to 80.8% (95% CI, 78.8%-82.8%) (P < .001). The overall difference in the evaluation time per slide between the digital system (mean, 21.7 seconds; 95% CI, 20.8-22.7 seconds) and microscopic examination (mean, 13.0 seconds; 95% CI, 12.4-13.5 seconds) was -8.8 seconds (95% CI, -9.8 to -7.7 seconds), but this difference decreased as pathologists became more familiar and experienced with the digital system; the difference between the time of evaluation on the last set of 20 slides for all pathologists when using the microscope and the digital system was 4.8 seconds (95% CI, 3.0-6.5 seconds).

Conclusions and Relevance

In this diagnostic study, an AI-augmented digital system significantly improved the accuracy of pathologic interpretation of colorectal polyps compared with microscopic assessment. If applied broadly to clinical practice, this tool may be associated with decreases in subsequent overuse and underuse of colonoscopy and thus with improved patient outcomes and reduced health care costs.

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

The histomorphological and molecular landscape of colorectal adenomas and serrated lesions

The 2019 WHO classification of digestive system tumors significantly reformed the classificatory definition of serrated lesions of the colorectal mucosa and added new essential diagnostic criteria for both conventional adenomas and hereditary gastrointestinal polyposis syndromes. Histopathological examination of colorectal adenocarcinoma precursors lesions represents an important segment of daily clinical practice in a pathology department and is essential for the implementation of current colorectal adenocarcinoma secondary prevention strategies. This overview will focus on a schematic histopathological and molecular classification of precursor lesions arising within colorectal mucosa.

Pathologists should probably forget about kappa. Percent agreement, diagnostic specificity and related metrics provide more clinically applicable measures of interobserver variability

Kappa statistics have been widely used in the pathology literature to compare interobserver diagnostic variability (IOV) among different pathologists but there has been limited discussion about the clinical significance of kappa scores. Five representative and recent pathology papers were queried using clinically relevant specific questions to learn how IOV was evaluated and how the clinical applicability of results was interpreted. The papers supported our anecdotal impression that pathologists usually assess IOV using Cohen's or Fleiss' kappa statistics and interpret the results using some variation of the scale proposed by Landis and Koch. The papers did not cite or propose specific guidelines to comment on the clinical applicability of results. The solutions proposed to decrease IOV included the development of better diagnostic criteria and additional educational efforts, but the possibility that the entities themselves represented a continuum of morphologic findings rather than distinct diagnostic categories was not considered in any of the studies. A dataset from a previous study of IOV reported by Thunnissen et al. was recalculated to estimate percent agreement among 19 international lung pathologists for the diagnosis of 74 challenging lung neuroendocrine neoplasms. Kappa scores and diagnostic sensitivity, specificity, positive and negative predictive values were calculated using the majority consensus diagnosis for each case as the gold reference diagnosis for that case. Diagnostic specificity estimates among multiple pathologists were > 90%, although kappa scores were considerably more variable. We explain why kappa scores are of limited clinical applicability in pathology and propose the use of positive and negative percent agreement and diagnostic specificity against a gold reference diagnosis to evaluate IOV among two and multiple raters, respectively.

Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides

IMPORTANCE

Histologic classification of colorectal polyps plays a critical role in screening for colorectal cancer and care of affected patients. An accurate and automated algorithm for the classification of colorectal polyps on digitized histopathologic slides could benefit practitioners and patients.

OBJECTIVE

To evaluate the performance and generalizability of a deep neural network for colorectal polyp classification on histopathologic slide images using a multi-institutional data set.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study used histopathologic slides collected from January 1, 2016, to June 31, 2016, from Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, with 326 slides used for training, 157 slides for an internal data set, and 25 for a validation set. For the external data set, 238 slides for 179 distinct patients were obtained from 24 institutions across 13 US states. Data analysis was performed from April 9 to November 23, 2019.

MAIN OUTCOMES AND MEASURES

Accuracy, sensitivity, and specificity of the model to classify 4 major colorectal polyp types: tubular adenoma, tubulovillous or villous adenoma, hyperplastic polyp, and sessile serrated adenoma. Performance was compared with that of local pathologists' at the point of care identified from corresponding pathology laboratories.

RESULTS

For the internal evaluation on the 157 slides with ground truth labels from 5 pathologists, the deep neural network had a mean accuracy of 93.5% (95% CI, 89.6%-97.4%) compared with local pathologists' accuracy of 91.4% (95% CI, 87.0%-95.8%). On the external test set of 238 slides with ground truth labels from 5 pathologists, the deep neural network achieved an accuracy of 87.0% (95% CI, 82.7%-91.3%), which was comparable with local pathologists' accuracy of 86.6% (95% CI, 82.3%-90.9%).

CONCLUSIONS AND RELEVANCE

The findings suggest that this model may assist pathologists by improving the diagnostic efficiency, reproducibility, and accuracy of colorectal cancer screenings.

Synthetic indicator of the impact of colorectal cancer screening programmes on incidence rates

OBJECTIVE

The impact of a screening programme on colorectal cancer (CRC) incidence in its target population depends on several variables, including coverage with invitations, participation rate, positivity rate of the screening test, compliance with an invitation to second-level assessment and endoscopists' sensitivity. We propose a synthetic indicator that may account for all the variables influencing the potential impact of a screening programme on CRC incidence.

DESIGN

We defined the 'rate of advanced adenoma on the target population' (AA-TAP) as the rate of patients who received a diagnosis of advanced adenoma within a screening programme, divided by the programme target population. We computed the AA-TAP for the CRC Italian screening programmes (biennial faecal immunochemical test, target population 50-69 year olds) using the data of the Italian National Survey from 2003 to 2016, overall and by region, and assessed the association between AA-TAP and CRC incidence fitting a linear regression between the trend of regional CRC incidence rates in 50-74 year old subjects and the cumulative AA-TAP.

RESULTS

In 2016, the AA-TAP at a national level was 105×100 000, whereas significant differences were observed between the northern and central regions (respectively 126 and 149×100 000) and the South and Islands (36×100 000). The cumulative AA-TAP from 2004 to 2012 was significantly correlated with the difference between CRC incidence rates in 2013-2014 and those in 2003-2004 (p=0.009).

CONCLUSION

The AA-TAP summarises into a single indicator the potential impact of a screening programme in reducing CRC incidence rates.

Forceps Biopsies Are Not Reliable in the Workup of Large Colorectal Lesions Referred for Endoscopic Resection: Should They Be Abandoned?

BACKGROUND

Biopsies are routinely obtained in the workup of large colorectal polyps before endoscopic resection.

OBJECTIVE

This study aimed to examine how reliable biopsies are in terms of reflecting the true histopathology of large colorectal polyps, in the clinical routine.

DESIGN

This is a retrospective study.

SETTINGS

Data from patients undergoing polypectomy of large colorectal polyps at the endoscopy unit, Skåne University Hospital Malmö, between January 2014 and December 2016 were scrutinized.

PATIENTS

A total of 485 colorectal lesions were biopsied within 1 year before complete endoscopic removal. Biopsy-obtained specimens were compared with completely resected specimens in terms of concordance and discordance and if the final result was upgraded or downgraded.

MAIN OUTCOME MEASURES

The primary outcome measured was the concordance between biopsy-obtained specimens and completely resected specimens.

RESULTS

Median lesion size was 3 cm (range 1-11). In 189 cases (39%), biopsies did not provide a correct dysplastic grade compared with final pathology after complete resection. One hundred forty-three cases (29%) and 46 cases (9%) were upgraded and downgraded. The percentage of cases with discordant biopsy results was 40% in cases with 1 biopsy taken and 38% in cases where multiple biopsies had been sampled. Time from biopsy to complete resection did not influence the erroneous outcome of biopsies. Notably, the percentage of discordant biopsy results was 37% and 35% in lesions measuring 1 to 2 cm and 2 to 4 cm. However, this percentage increased to 48% in colorectal lesions larger than 4 cm.

LIMITATIONS

This study was designed to reflect the clinical routine, the number of biopsies obtained and forceps technique were hence not standardized, which constitutes a limitation.

CONCLUSIONS

This study demonstrates that cancer-negative forceps biopsies of large colorectal polyps, referred for endoscopic resection, are not reliable. Considering that endoscopic resection of lesions containing superficial cancer is plausible, the clinical value of forceps biopsies in lesions suitable for endoscopic resection is questionable. See Video Abstract at http://links.lww.com/DCR/A984. LAS BIOPSIAS CON FÓRCEPS NO SON CONFIABLES EN EL ESTUDIO DE LAS LESIONES COLORRECTALES GRANDES REFERIDAS PARA RESECCIÓN ENDOSCÓPICA: ¿DEBERÍAN ABANDONARSE?: Las biopsias se obtienen de forma rutinaria en el estudio de pólipos colorrectales grandes previo a resección endoscópica.

OBJETIVO

Analizar que tan confiables son las biopsias en cuanto a reflejar la verdadera histopatología de los pólipos colorrectales grandes, en la rutina clínica. DISEÑO:: Este es un estudio retrospectivo.

AJUSTES

Los datos de pacientes sometidos a polipectomía de pólipos colorrectales grandes en la unidad de endoscopia, en Skåne University Hospital Malmö, entre enero de 2014 y diciembre de 2016 fueron examinados.

PACIENTES

Un total de 485 lesiones colorrectales se biopsiaron dentro de un año antes de la resección endoscópica completa. Las muestras obtenidas mediante biopsia se compararon con las muestras completas resecadas en términos de concordancia y discordancia, y si el resultado final ascendió o disminuyó de categoría.

PRINCIPALES MEDIDAS DE RESULTADO

Concordancia entre muestras obtenidas mediante biopsia y muestras completamente resecadas.

RESULTADOS

La mediana de tamaño de lesiones fue de 3 cm (rango 1-11). En 189 casos (39%) las biopsias no proporcionaron un grado de displasia correcto en comparación con la patología final después de la resección completa. 143 casos (29%) y 46 casos (9%) ascendieron y descendieron de categoría, respectivamente. El porcentaje de casos con resultados de biopsia discordantes fue del 40% en los casos con una sola biopsia tomada y del 38% en los casos en los que se tomaron múltiples biopsias. El tiempo desde la biopsia hasta la resección completa no influyó en el resultado erróneo de las biopsias. Notablemente, el porcentaje de resultados de biopsia discordantes fue de 37% y 35% en lesiones que midieron 1-2 cm y 2-4 cm, respectivamente. Sin embargo, este porcentaje aumentó a 48% en lesiones colorrectales mayores de 4 cm.

LIMITACIONES

Este estudio se diseñó para reflejar la rutina clínica, el número de biopsias obtenidas y la técnica de fórceps no fueron estandarizadas, lo que constituye una limitación.

CONCLUSIONES

Este estudio demuestra que las biopsias con fórceps negativas a cáncer, de pólipos colorrectales grandes referidas para resección endoscópica, no son confiables. Teniendo en cuenta que la resección endoscópica de lesiones que contienen cáncer superficial es posible, el valor clínico de las biopsias con fórceps en lesiones aptas para la resección endoscópica es cuestionable. Vea el Resumen en video en http://links.lww.com/DCR/A984.

Intraobserver and Interobserver Variability in the Assessment of Dysplasia in Ampullary Mucosal Biopsies

Endoscopic mucosal biopsies of the ampulla of Vater (AmpBx) are obtained to histologically assess for dysplasia or carcinoma. However, biopsy material is often scant and a host of factors can induce histologic changes that pose diagnostic challenges. We sought to investigate observer variability in interpretation of AmpBx and the impact clinical data may have on diagnostic interpretation. Thirty-one cases from institutional archives were selected, including 12 cases of reactive atypia (RA), 8 indefinite for dysplasia (ID), and 11 showing low-grade dysplasia (LGD). Slides were independently reviewed at 3 time points with and without clinical information by 6 pathologists who categorized the biopsies RA, ID, or LGD. Following the reviews, intraobserver and interobserver agreement was assessed. Review of AmpBx without clinical data showed fair (κ, 0.27), poor (κ, 0.07), and good (κ, 0.42) interobserver agreement for diagnoses of RA, ID, and LGD, respectively. Interobserver agreement improved for LGD (κ, 0.66 and 0.73) when clinical information was provided; however, agreement remained fair for RA (κ, 0.4 and 0.42) and poor-to-fair for ID (κ, 0.17 and 0.25). When follow-up data were reviewed, all cases that reached unanimous agreement had that diagnosis substantiated by subsequent endoscopic or histologic findings. The same was true of 13 of 19 cases that reached majority consensus. Given the potential clinical consequences of these diagnoses combined with the significant intraobserver and interobserver variability found in this study, we conclude that better-defined diagnostic criteria and consensus reads on difficult cases would assist in the histologic assessment of these challenging cases.

Decrease of variation in the grading of dysplasia in colorectal adenomas with a national e-learning module

AIMS

Variation in health-care is undesirable, as this is potentially harmful for patients. In the Netherlands, an e-learning module was developed to standardise pathological evaluation of colorectal adenomas. We studied the effect of e-learning on interlaboratory variability in grading of dysplasia in screened conventional colorectal adenomas.

METHODS AND RESULTS

A cross-sectional retrospective study was performed, including all colorectal adenomas from the Dutch population-based colorectal cancer screening programme, retrieved from the Dutch Pathology Registry (PALGA) from January 2014 to July 2015. The e-learning tool, commissioned by the National Institute for Public Health, was implemented among screening pathologists from October 2014. Proportions of high-grade dysplasia (HGD) were compared before (January-July 2014) and after implementation (October 2014-July 2015) of the e-learning module. Interlaboratory variation was assessed by multilevel mixed-effects analysis. In total, 20 713 colonoscopies (20 546 patients) were performed after a positive faecal immunochemical screening test, resulting in the inclusion of 56 355 conventional adenomas from 37 pathology laboratories. Before implementation, 12 614 adenomas were diagnosed, including 4.3% with HGD. After implementation, 43 741 adenomas were diagnosed, and the HGD proportion decreased to 3.9%. Univariable analysis showed less deviant proportions of HGD after implementation in 62% of the laboratories (P = 0.019). Multilevel analysis confirmed decreased variation in the risk of diagnosing HGD (P = 0.021).

CONCLUSIONS

Interlaboratory variability in grading HGD in colorectal adenomas after a positive screening test decreased after implementation of an e-learning module for pathologists. We therefore conclude that e-learning has a favourable influence on decreasing diagnostic variability, making this a relevant strategy for health-care standardisation.

Diagnostic and reporting issues of preneoplastic polyps of the large intestine with early carcinoma

Premalignant polyps of the large intestine are common specimens in surgical pathology. They consist of several different subtypes identifiable by histological criteria that are associated with different molecular characteristics and with the development of different types of colorectal carcinoma. The most common of these is the conventional adenoma, which most commonly leads to carcinomas with a low degree of methylation (CIMP-L) that are microsatellite stable. In Lynch syndrome patients these polyps lead to CIMP-L carcinomas that are microsatellite instable. The second most common is the sessile serrated adenoma, which leads to carcinomas with a high degree of methylation (CIMP-H) that may be either microsatellite stable or instable. The least common premalignant polyp is the traditional serrated adenoma, which can lead to either CIMP-L or CIMP-H carcinomas, most often microsatellite stable. This paper will review the histological features of these lesions, discuss problems in diagnosis and discuss the role of histology in management.

Radiomics Evaluation of Histological Heterogeneity Using Multiscale Textures Derived From 3D Wavelet Transformation of Multispectral Images

Purpose

Colorectal cancer (CRC) is markedly heterogeneous and develops progressively toward malignancy through several stages which include stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) or precursor cancerous lesion, and carcinoma (CA). Identification of the malignancy stage of CRC pathology tissues (PT) allows the most appropriate therapeutic intervention.

Methods

This study investigates multiscale texture features extracted from CRC pathology sections using 3D wavelet transform (3D-WT) filter. Multiscale features were extracted from digital whole slide images of 39 patients that were segmented in a pre-processing step using an active contour model. The capacity for multiscale texture to compare and classify between PTs was investigated using ANOVA significance test and random forest classifier models, respectively.

Results

12 significant features derived from the multiscale texture (i.e., variance, entropy, and energy) were found to discriminate between CRC grades at a significance value of p < 0.01 after correction. Combining multiscale texture features lead to a better predictive capacity compared to prediction models based on individual scale features with an average (±SD) classification accuracy of 93.33 (±3.52)%, sensitivity of 88.33 (± 4.12)%, and specificity of 96.89 (± 3.88)%. Entropy was found to be the best classifier feature across all the PT grades with an average of the area under the curve (AUC) value of 91.17, 94.21, 97.70, 100% for ST, BH, IN, and CA, respectively.

Conclusion

Our results suggest that multiscale texture features based on 3D-WT are sensitive enough to discriminate between CRC grades with the entropy feature, the best predictor of pathology grade.

Evolution of Premalignant Disease

Where does cancer come from? Although the cell-of-origin is difficult to pinpoint, cancer clones harbor information about their clonal ancestries. In an effort to find cells before they evolve into a life-threatening cancer, physicians currently diagnose premalignant diseases at frequencies that substantially exceed those of clinical cancers. Cancer risk prediction relies on our ability to distinguish between which premalignant features will lead to cancer mortality and which are characteristic of inconsequential disease. Here, we review the evolution of cancer from premalignant disease, and discuss the concept that even phenotypically normal cell progenies inherently gain more malignant potential with age. We describe the hurdles of prognosticating cancer risk in premalignant disease by making reference to the underlying continuous and multivariate natures of genotypes and phenotypes and the particular challenge inherent in defining a cell lineage as "cancerized."

Interobserver agreement among cytopathologists in the evaluation of pancreatic endoscopic ultrasound-guided fine needle aspiration cytology specimens

BACKGROUND AND AIMS

Endoscopic ultrasound with fine needle aspiration (EUS-FNA) has become the standard of care in the evaluation of solid pancreatic lesions. Limited data exist on interobserver agreement (IOA) among cytopathologists in assessing solid pancreatic EUS-FNA specimens. This study aimed to evaluate IOA among cytopathologists in assessing EUS-FNA cytology specimens of solid pancreatic lesions using a novel standardized scoring system and to assess individual clinical and cytologic predictors of IOA.

METHODS

Consecutive patients who underwent EUS-FNA of solid pancreatic lesions at a tertiary care referral center were included. EUS-FNA slides were evaluated by four blinded cytopathologists using a standardized scoring system that assessed final cytologic diagnosis and quantitative (number of nucleated/diagnostic cells) and qualitative (bloodiness, inflammation/necrosis, contamination, artifact) cytologic parameters. Final clinical diagnosis was based on final cytology, surgical pathology, or 1-year clinical follow-up. IOA was calculated using multi-rater kappa (κ) statistics. Bivariate analyses were performed comparing cases with and without uniform agreement among the cytopathologists followed by logistic regression with backward elimination to model likelihood of uniform agreement.

RESULTS

Ninety-nine patients were included (49 % males, mean age 64 years, mean lesion size 26 mm). IOA for final diagnosis was moderate (κ = 0.45, 95 % confidence interval (CI) 0.4 - 0.49) with minimal improvement when combining suspicious and malignant diagnoses (κ = 0.54, 95 %CI 0.49 - 0.6). The weighted kappa value for overall diagnosis was 0.65 (95 %CI 0.54 - 0.76). IOA was slight to fair (κ = 0.04 - 0.32) for individual cytologic parameters. A final clinical diagnosis of malignancy was the most significant predictor of agreement [OR 3.99 (CI 1.52 - 10.49)].

CONCLUSIONS

Interobserver agreement among cytopathologists for pancreatic EUS-FNA specimens is moderate-substantial for the final cytologic diagnosis. The final clinical diagnosis of malignancy was the strongest predictor of agreement. These results have significant implications for patient management and need to be validated in future trials.

Watch and wait policy after preoperative radiotherapy for rectal cancer; management of residual lesions that appear clinically benign

BACKGROUND

During an ongoing phase II observational study on watch and wait policy in rectal cancer, a substantial number of patients presented residual lesion after radiotherapy with a clinical benign appearance. This article aims to discuss the clinical significance of such findings.

MATERIALS AND METHODS

Main entry criteria were age ≥70 years and small tumour (≤5 cm and ≤60% of circumferential involvement) located in the low rectum. Patients received chemoradiation (50 Gy, 2 Gy per fraction concomitantly with a 5-Fu bolus and leucovorin) or 5 × 5 Gy if considered unfit for chemotherapy. Patients with clinical complete response (cCR) were observed. Those with persistent tumours underwent transanal endoscopic microsurgery [TEM] if the baseline tumour was ≤3 cm and cN0 or total mesorectal excision.

RESULTS

The watch and wait procedure was used in 11 out of the total 35 patients (31%) with a cCR; 17 patients (49%) with residual tumours that appeared clinically malignant were referred for TEM or abdominal surgery. In the remaining seven (20%), the residual tumour clinically appeared benign. Of these, there were two invasive cancers, four high-grade dysplasias and one low-grade dysplasia. The five patients with dysplasia, underwent local lesion resection without recurrence within a median of 11 months follow-up.

CONCLUSIONS

The majority of lesions that appeared clinically benign after radio(chemo)therapy were also benign on pathological examination. Thus, local excision of such lesions should be considered.