A framework for evaluating diagnostic discordance in pathology discovered during research studies

Analysis of Discrepancy Index between Clinical and Histopathological Diagnosis of Oral Lesions

OBJECTIVE

To analyze the discrepancy index between the clinical and histological diagnosis of oral lesions.

MATERIALS AND METHOD

A sample of 910 cases from year 2013-2021 were analyzed using non probability convenience sampling technique. This included patient records and histopathological reports of patients treated at IIDC & H and FUCD & H. Clinical presentations were classified under five categories; growth/swelling, vesico-ulcerative, white, red/pigmented, and cystic lesions. To evaluate the details of diagnostic discrepancies, the data was categorized into 4 major groups: 1) Neoplastic-Neoplastic,2) Non-Neoplastic-Non-Neoplastic ,3) Neoplastic-Non-Neoplastic and 4) Non-Neoplastic-Neoplastic. The association between clinical diagnosis and histopathological diagnosis was calculated by using pearson chi square test and statistical significance was considered with the p value less than (0.05).

RESULTS

Most common clinical presentation was swelling/growth; 601 (66%), followed by ulceration; 223 (24.5%). There were 528 (58%) incisional and 382 (42%) excisional biopsies. The definitive diagnosis based on histopathological findings showed malignant neoplasms as the commonest category; 287 (31.5%) followed by inflammatory/reactive lesions 271 (29.8%). A consensus was noted between the clinical and histologic diagnosis in 74.8% cases, while a discrepancy index of 25.1 % was calculated. Regarding diagnostic discrepancy among four major categories of our research, maximum discrepancy was noted in neoplastic-nonneoplastic category (29.6%) and minimum discrepancy was noted in malignant - benign category (2.7%).  Statistically significant difference between the clinical and histopathological diagnosis was observed with a p value of 0.000.

CONCLUSION

Considerable amount of diagnostic discordance was observed in all types of pathologies analyzed in the study.

More scanning, but not zooming, is associated with diagnostic accuracy in evaluating digital breast pathology slides

Diagnoses of medical images can invite strikingly diverse strategies for image navigation and visual search. In computed tomography screening for lung nodules, distinct strategies, termed scanning and drilling, relate to both radiologists' clinical experience and accuracy in lesion detection. Here, we examined associations between search patterns and accuracy for pathologists (N = 92) interpreting a diverse set of breast biopsy images. While changes in depth in volumetric images reveal new structures through movement in the z-plane, in digital pathology changes in depth are associated with increased magnification. Thus, "drilling" in radiology may be more appropriately termed "zooming" in pathology. We monitored eye-movements and navigation through digital pathology slides to derive metrics of how quickly the pathologists moved through XY (scanning) and Z (zooming) space. Prior research on eye-movements in depth has categorized clinicians as either "scanners" or "drillers." In contrast, we found that there was no reliable association between a clinician's tendency to scan or zoom while examining digital pathology slides. Thus, in the current work we treated scanning and zooming as continuous predictors rather than categorizing as either a "scanner" or "zoomer." In contrast to prior work in volumetric chest images, we found significant associations between accuracy and scanning rate but not zooming rate. These findings suggest fundamental differences in the relative value of information types and review behaviors across two image formats. Our data suggest that pathologists gather critical information by scanning on a given plane of depth, whereas radiologists drill through depth to interrogate critical features.

Infrared Microspectroscopy With Multivariate Analysis to Differentiate Oral Hyperplasia From Squamous Cell Carcinoma: A Proof of Concept for Early Diagnosis

BACKGROUND AND OBJECTIVES

Despite having numerous advances in therapeutics, mortality and morbidity due to oral cancer incidence are still very high. Early detection can improve the chances of survival in most patients. However, diagnosis at early stages can be challenging as premalignant conditions are usually asymptomatic. Currently, histological assessment remains the gold standard for diagnosis. Early diagnosis poses challenges to pathologists due to less severe morphological changes associated with early stages. Therefore, a fast and robust method of detection based on molecular changes is needed for early diagnosis. © 2021 Wiley Periodicals LLC.

STUDY DESIGN/MATERIAL AND METHODS

In the present study, Fourier transform infrared (FTIR) spectroscopic imaging has been used to differentiate early-stage oral hyperplasia from adjacent normal (AN) and oral squamous cell carcinoma (OSCC). Hyperplasia is often considered as an initial event in the pathogenesis of oral cancer and OSCC is the most common advanced stage of malignancy. Differentiating normal versus hyperplasia and hyperplasia versus OSCC can remain quite challenging on occasion using conventional staining as the histological assessment is based on morphological changes.

RESULTS

Unsupervised hierarchical cluster analysis (UHCA) has been performed on FTIR images of multiple tissues together that provided some degree of classification among tissue groups. The AN epithelium clustered distinctively using UHCA from both hyperplasia and grades 1 and 2 of OSCC. An increase in the content of DNA, denaturation of protein, and altered lipid structures were more clearly elucidated with spectral analysis.

CONCLUSION

This study demonstrates a simple strategy to differentiate early-stage oral hyperplasia from AN and OSCC using UHCA. This study also proposes a future alternative method where FTIR imaging can be used as a diagnostic tool for cancer at early stages.

ATR-FTIR spectroscopy as adjunct method to the microscopic examination of hematoxylin and eosin-stained tissues in diagnosing lung cancer

Lung cancer remains the leading cause of cancer-related death worldwide. Since prognosis and treatment outcomes rely on fast and accurate diagnosis, there is a need for more cost-effective, sensitive, and specific method for lung cancer detection. Thus, this study aimed to determine the ability of ATR-FTIR in discriminating malignant from benign lung tissues and evaluate its concordance with H&E staining. Three (3) 5μm-thick sections were cut from formalin fixed paraffin embedded (FFPE) cell or tissue blocks from patients with lung lesions. The outer sections were H&E-stained and sent to two (2) pathologists to confirm the histopathologic diagnosis. The inner section was deparaffinized by standard xylene method and then subjected to ATR-FTIR analysis. Distinct spectral profiles that distinguished (p<0.05) one sample from another, called the "fingerprint region", were observed in five (5) peak patterns representing the amides, lipids, and nucleic acids. Principal component analysis and hierarchical cluster analysis evidently clustered the benign from malignant tissues. ATR-FTIR showed 97.73% sensitivity, 92.45% specificity, 94.85% accuracy, 91.49% positive predictive value and 98.00% negative predictive value in discriminating benign from malignant lung tissue. Further, strong agreement was observed between histopathologic readings and ATR-FTIR analysis. This study shows the potential of ATR-FTIR spectroscopy as a potential adjunct method to the gold standard, the microscopic examination of hematoxylin and eosin (H&E)-stained tissues, in diagnosing lung cancer.

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

Whole slide digital imaging technology enables researchers to study pathologists' interpretive behavior as they view digital slides and gain new understanding of the diagnostic medical decision-making process. In this study, we propose a simple yet important analysis to extract diagnostically relevant regions of interest (ROIs) from tracking records using only pathologists' actions as they viewed biopsy specimens in the whole slide digital imaging format (zooming, panning, and fixating). We use these extracted regions in a visual bag-of-words model based on color and texture features to predict diagnostically relevant ROIs on whole slide images. Using a logistic regression classifier in a cross-validation setting on 240 digital breast biopsy slides and viewport tracking logs of three expert pathologists, we produce probability maps that show 74 % overlap with the actual regions at which pathologists looked. We compare different bag-of-words models by changing dictionary size, visual word definition (patches vs. superpixels), and training data (automatically extracted ROIs vs. manually marked ROIs). This study is a first step in understanding the scanning behaviors of pathologists and the underlying reasons for diagnostic errors.

Surgical implications and variability in the use of the flat epithelial atypia diagnosis on breast biopsy specimens

OBJECTIVES

Flat epithelial atypia (FEA) is a relatively new diagnostic term with uncertain clinical significance for surgical management. Any implied risk of invasive breast cancer associated with FEA is contingent upon diagnostic reproducibility, yet little is known regarding its use.

MATERIALS AND METHODS

Pathologists in the Breast Pathology Study interpreted one of four 60-case test sets, one slide per case, constructed from 240 breast biopsy specimens. An electronic data form with standardized diagnostic categories was used; participants were instructed to indicate all diagnoses present. We assessed participants' use of FEA as a diagnostic term within: 1) each test set; 2) 72 cases classified by reference as benign without FEA; and 3) six cases classified by reference as FEA. 115 pathologists participated, providing 6900 total independent assessments.

RESULTS

Notation of FEA ranged from 0% to 35% of the cases interpreted, with most pathologists noting FEA on 4 or more test cases. At least one participant noted FEA in 34 of the 72 benign non-FEA cases. For the 6 reference FEA cases, participant agreement with the case reference FEA diagnosis ranged from 17% to 52%; diagnoses noted by participating pathologists for these FEA cases included columnar cell hyperplasia, usual ductal hyperplasia, atypical lobular hyperplasia, and atypical ductal hyperplasia.

CONCLUSIONS

We observed wide variation in the diagnosis of FEA among U.S. pathologists. This suggests that perceptions of diagnostic criteria and any implied risk associated with FEA may also vary. Surgical excision following a core biopsy diagnosis of FEA should be reconsidered and studied further.

A Randomized Study Comparing Digital Imaging to Traditional Glass Slide Microscopy for Breast Biopsy and Cancer Diagnosis

Background:

Digital whole slide imaging may be useful for obtaining second opinions and is used in many countries. However, the U.S. Food and Drug Administration requires verification studies.

Methods:

Pathologists were randomized to interpret one of four sets of breast biopsy cases during two phases, separated by ≥9 months, using glass slides or digital format (sixty cases per set, one slide per case, n = 240 cases). Accuracy was assessed by comparing interpretations to a consensus reference standard. Intraobserver reproducibility was assessed by comparing the agreement of interpretations on the same cases between two phases. Estimated probabilities of confirmation by a reference panel (i.e., predictive values) were obtained by incorporating data on the population prevalence of diagnoses.

Results:

Sixty-five percent of responding pathologists were eligible, and 252 consented to randomization; 208 completed Phase I (115 glass, 93 digital); and 172 completed Phase II (86 glass, 86 digital). Accuracy was slightly higher using glass compared to digital format and varied by category: invasive carcinoma, 96% versus 93% (P = 0.04); ductal carcinoma in situ (DCIS), 84% versus 79% (P < 0.01); atypia, 48% versus 43% (P = 0.08); and benign without atypia, 87% versus 82% (P < 0.01). There was a small decrease in intraobserver agreement when the format changed compared to when glass slides were used in both phases (P = 0.08). Predictive values for confirmation by a reference panel using glass versus digital were: invasive carcinoma, 98% and 97% (not significant [NS]); DCIS, 70% and 57% (P = 0.007); atypia, 38% and 28% (P = 0.002); and benign without atypia, 97% and 96% (NS).

Conclusions:

In this large randomized study, digital format interpretations were similar to glass slide interpretations of benign and invasive cancer cases. However, cases in the middle of the spectrum, where more inherent variability exists, may be more problematic in digital format. Future studies evaluating the effect these findings exert on clinical practice and patient outcomes are required.

Diagnostic concordance among pathologists interpreting breast biopsy specimens

IMPORTANCE

A breast pathology diagnosis provides the basis for clinical treatment and management decisions; however, its accuracy is inadequately understood.

OBJECTIVES

To quantify the magnitude of diagnostic disagreement among pathologists compared with a consensus panel reference diagnosis and to evaluate associated patient and pathologist characteristics.

DESIGN, SETTING, AND PARTICIPANTS

Study of pathologists who interpret breast biopsies in clinical practices in 8 US states.

EXPOSURES

Participants independently interpreted slides between November 2011 and May 2014 from test sets of 60 breast biopsies (240 total cases, 1 slide per case), including 23 cases of invasive breast cancer, 73 ductal carcinoma in situ (DCIS), 72 with atypical hyperplasia (atypia), and 72 benign cases without atypia. Participants were blinded to the interpretations of other study pathologists and consensus panel members. Among the 3 consensus panel members, unanimous agreement of their independent diagnoses was 75%, and concordance with the consensus-derived reference diagnoses was 90.3%.

MAIN OUTCOMES AND MEASURES

The proportions of diagnoses overinterpreted and underinterpreted relative to the consensus-derived reference diagnoses were assessed.

RESULTS

Sixty-five percent of invited, responding pathologists were eligible and consented to participate. Of these, 91% (N = 115) completed the study, providing 6900 individual case diagnoses. Compared with the consensus-derived reference diagnosis, the overall concordance rate of diagnostic interpretations of participating pathologists was 75.3% (95% CI, 73.4%-77.0%; 5194 of 6900 interpretations). Among invasive carcinoma cases (663 interpretations), 96% (95% CI, 94%-97%) were concordant, and 4% (95% CI, 3%-6%) were underinterpreted; among DCIS cases (2097 interpretations), 84% (95% CI, 82%-86%) were concordant, 3% (95% CI, 2%-4%) were overinterpreted, and 13% (95% CI, 12%-15%) were underinterpreted; among atypia cases (2070 interpretations), 48% (95% CI, 44%-52%) were concordant, 17% (95% CI, 15%-21%) were overinterpreted, and 35% (95% CI, 31%-39%) were underinterpreted; and among benign cases without atypia (2070 interpretations), 87% (95% CI, 85%-89%) were concordant and 13% (95% CI, 11%-15%) were overinterpreted. Disagreement with the reference diagnosis was statistically significantly higher among biopsies from women with higher (n = 122) vs lower (n = 118) breast density on prior mammograms (overall concordance rate, 73% [95% CI, 71%-75%] for higher vs 77% [95% CI, 75%-80%] for lower, P < .001), and among pathologists who interpreted lower weekly case volumes (P < .001) or worked in smaller practices (P = .034) or nonacademic settings (P = .007).

CONCLUSIONS AND RELEVANCE

In this study of pathologists, in which diagnostic interpretation was based on a single breast biopsy slide, overall agreement between the individual pathologists' interpretations and the expert consensus-derived reference diagnoses was 75.3%, with the highest level of concordance for invasive carcinoma and lower levels of concordance for DCIS and atypia. Further research is needed to understand the relationship of these findings with patient management.