Research Insights & Reviews: Pathology

Biosensors for oral cancer detection: a scoping review

Achmad Syawqie — 2026-09-05

This scoping review summarizes recent advances in biosensor technologies for the early, non-invasive detection of oral cancer, with a focus on salivary biomarkers and point-of-care platforms. Five databases (PubMed, Scopus, Web of Science, ProQuest, and EBSCO) were searched for studies published from 2019 to 2025. After duplicate removal and screening, nine eligible original studies were charted. The included platforms were primarily electrochemical, optical, and transistor-based biosensors targeting biomarkers such as interleukin-8 (IL-8), cytokeratin fragment 21.1 (Cyfra 21.1), cancerous inhibitor of PP2A (CIP2A/P90), and high-risk HPV genotypes. Reported limits of detection were frequently in the femtomolar range, although reporting of assay time, sample volume, and clinical diagnostic accuracy was inconsistent. Overall, biosensors show strong analytical potential for oral cancer screening; however, translation to routine clinical use will require standardized analytical validation, careful control of preanalytical saliva variables, and well-designed multicenter clinical studies reporting sensitivity and specificity against appropriate reference standards.

Construction of an integrated diagnostic-therapeutic model for prostate cancer using rapid multiplex immunohistochemistry

Yang Luan — 2026-09-05

Objective: To explore the feasibility and clinical application value of constructing an integrated model for the diagnosis and treatment of prostate cancer based on rapid multiplex immunohistochemistry (RMI) combined with intraoperative frozen section examination (IFSE).
Methods: A total of 50 patients with high suspicion of localized prostate cancer were prospectively enrolled at our hospital between January and November 2025. All patients underwent mpMRI/TRUS fusion‑guided transperineal targeted biopsy. Tissue specimens obtained during biopsy were processed for both IFSE and RMI. The RMI panel included antibodies against P504S, P63, 34βE12, and CK5/6. Patients with an intraoperative pathological diagnosis of prostate cancer proceeded directly to robot‑assisted laparoscopic radical prostatectomy (RARP). Those with negative intraoperative findings underwent systematic biopsy. The diagnostic performance of this model was evaluated using postoperative whole- mount histopathology or conventional pathology from systematic biopsy as the reference standard.
Results: The overall pathological positivity rate in this study was 86.00% (43/50). Of these, 39 patients were intraoperatively diagnosed with prostate cancer by rapid pathology and subsequently underwent immediate RARP. IFSE alone diagnosed prostate cancer in 37 cases, with a diagnostic sensitivity of 94.87% (37/39). The concordance rate of Gleason score with postoperative pathology was 81.08%. IFSE combined with RMI diagnosed prostate cancer in all 39 cases, each later confirmed by postoperative pathology following RARP, yielding a diagnostic sensitivity of 100%. The Gleason score concordance rate improved to 97.44%. The false-negative rate was 5.13% for IFSE alone, compared to 0% for the combined IFSE and RMI approach. The mean time to obtain pathological results was 29.80 ± 3.92 minutes for IFSE alone, and 29.93 ± 2.35 minutes for IFSE combined with RMI, indicating no significant prolongation. All 39 patients who underwent immediate RARP had a mean hospital stay of 7.31 ± 1.41 days, with only one hospitalization episode per patient.

Conclusion: RMI effectively compensates for the limitations of IFSE alone. The combined application of IFSE and RMI within an integrated diagnostic–therapeutic model for prostate cancer significantly improves intraoperative diagnostic sensitivity and Gleason score accuracy without substantially extending waiting time.

Immunohistochemical expression of Stomatin-Like protein 2 and trophoblast cell surface antigen 2 in papillary thyroid carcinoma in comparison to other thyroid lesions

Ahmed A. Elmetwally — 2026-05-13

Background: Thyroid carcinoma is a common tumor affecting the Egyptian population accounting for 2% of newly diagnosed cases of cancer in Egypt yearly and constituting 7.73% of 5 year prevalence in Egypt. Stomatin-Like Protein 2 (SLP-2) and Trophoblast Cell Surface Antigen 2 (TROP-2) antibodies are both expressed in some types of thyroid carcinomas.

Aim: To evaluate immunohistochemical expression of SLP-2 and TROP-2 in papillary thyroid carcinoma and other thyroid lesions, assess association of both immunohistochemical markers with variable demographic and histopathological parameters and assess the diagnostic utility of both markers in thyroid carcinomas.

Subjects and methods: The current study is a cross-sectional study with analytical component, performed in the Pathology laboratory of Suez Canal University Hospital on 144 samples of formalin fixed paraffin embedded blocks of thyroid cases, including 79 cases of PTC and 65 cases of other thyroid lesions during the period from January 2016 to May 2019.

Results: SLP-2 and TROP-2 have shown positive expression in 91.1% and 82.3% respectively in PTC cases. Compared to positive expressions in 38.5% and 7.7% respectively in other thyroid cases. The positivity of both markers has a statistically significant association with PTC cases, compared to other thyroid lesions p-value < 0.05). The sensitivity of combined positively expressed markers is 79.75%, specificity is 98.46%, positive predictive value is 98.44, the negative predictive value is 80% and the test accuracy is raised to 88.19 for discrimination between PTC and other thyroid lesions. There was no statistically significant association between all histopathological parameters and the expression of SLP-2 and TROP-2.

Conclusions: SLP-2 and TROP-2 may be promising diagnostic markers of PTC carcinoma. Further studies are needed to fully evaluate the prognostic significance of SLP-2 and TROP-2 expression in PTC and other thyroid lesions.

Clinicopathological characteristics across the HER2 expression spectrum in invasive breast carcinoma of no special type: a single-centre retrospective study

Aditi Tiwary — 2026-09-05

Background: The efficacy of trastuzumab deruxtecan in HER2-low metastatic breast cancer has shifted HER2 classification from a binary framework toward a continuous expression spectrum that now includes HER2-low and HER2-ultralow categories. Accurate characterization of these subgroups is essential for therapeutic stratification, yet their clinicopathological profiles remain incompletely defined, particularly in the Indian population. This study compared the clinicopathological features and short-term outcomes of invasive breast carcinoma of no special type across four HER2-defined subgroups.

Methods: Three hundred cases of invasive breast carcinoma of no special type diagnosed on core needle biopsy at a tertiary centre in southern India (2022–2023) were classified into HER2-positive, HER2-low, HER2-ultralow, and HER2-negative subgroups using immunohistochemistry with fluorescence in situ hybridisation confirmation. Associations were evaluated using chi-square tests, Kruskal-Wallis tests, binary logistic regression, correspondence analysis, and Kaplan-Meier survival analysis.

Results: HER2-low constituted the largest subgroup at 51.7%, followed by HER2-positive (32.3%), HER2-ultralow (9.0%), and HER2-negative (7.0%). HER2-low tumours demonstrated significantly higher oestrogen receptor (74.8%) and progesterone receptor (61.3%) positivity, lower Nottingham grade, and a greater proportion of low Ki-67 proliferation index compared to the other subgroups. Age, menopausal status, clinical stage, tumour size, lymphovascular invasion, perineural invasion, and stromal tumour-infiltrating lymphocyte density did not differ significantly across the four subgroups. On multivariate logistic regression, progesterone receptor positivity was the sole independent predictor of HER2-low status (odds ratio 1.92, 95% confidence interval 1.05–3.50, p = 0.034). The triple-negative phenotype increased progressively from HER2-low (25.2%) through HER2-ultralow (40.7%) to HER2-negative (57.1%). Correspondence analysis placed HER2-ultralow in an intermediate position between HER2-low and HER2-negative. No significant survival differences were observed over a median follow-up of 23 months.

Conclusions: HER2-low tumours represent the majority of invasive breast carcinomas in this Indian cohort and exhibit a distinct profile defined by hormone receptor enrichment, lower grade, and reduced proliferative activity. The intermediate phenotype of HER2-ultralow supports its recognition as a biologically separate category. These findings highlight the importance of standardised HER2 reporting across the full expression spectrum to guide antibody-drug conjugate therapy eligibility.

Detection of collagen band–associated regions in H&E-stained colonic biopsies of collagenous colitis patients using superpixel-based feature extraction and neural network classification

Vytautas Kiudelis — 2026-08-06

Background: Collagenous colitis (CC) is diagnosed histologically and is characterised by a thickened subepithelial collagen band together with inflammatory and epithelial changes. Although routine haematoxylin and eosin (H&E) staining is sufficient for diagnosis in most cases, visual assessment of the collagen band can be challenging in borderline or heterogeneous specimens. Additional stains may be required in diagnostically difficult situations.

The aim: To develop a machine-learning–based algorithm for detecting subepithelial collagen band-associated regions in routine H&E-stained colonic biopsy images as a decision-support tool for histopathological assessment.

Methods: H&E-stained colonic biopsy specimens from 36 patients with histologically confirmed CC were imaged at 20 × magnification (1392 × 1040 pixels). Images were segmented into 1,000 superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm. Superpixels overlapping with expert-provided rough annotations of the collagen band were labelled and characterised using normalised RGB histograms. A feed-forward neural network classifier (three hidden layers, 10 neurons per layer) was trained to distinguish collagen band–associated from non-collagen regions. Class imbalance was addressed by data augmentation of minority-class superpixels. Post-processing with connected-component size filtering was applied to enforce spatial continuity. Superpixel-level performance was evaluated quantitatively, and image-level outputs were assessed using expert acceptability scoring.

Results: The classifier achieved a superpixel-wise accuracy of 0.928 (sensitivity 0.898, specificity 0.953). Size-based post-processing substantially reduced isolated false-positive detections. At the image level, the final algorithm achieved an acceptability accuracy of 0.846 according to expert evaluation. The model successfully highlighted subepithelial collagen band–associated regions consistent with expert annotations but did not model additional diagnostic features required for complete CC diagnosis.

Conclusion: Our superpixel-based neural network highlights collagen-rich regions in H&E-stained colonic biopsies, offering decision support for pathologists. As diagnosis of collagenous colitis requires broader histopathological and clinical context, this method is intended as a decision-support tool rather than a stand-alone diagnostic solution.