Combination therapies have largely replaced monotherapies in oncology. Thermal therapy has been developed as an adjunctive treatment owing to its safety and high compatibility with established theraypy options. However, bulky equipment and non-standardized protocols have limited its clinical use. The emergence of gold nanoparticle (AuNP)-based photothermal therapy (PTT) offers a simpler approach using injectable formulations and portable near-infrared (NIR) laser devices. However, poor excretion of AuNPs raises safety concerns and hinders translation. Here, we designed a gold nanoplatform that retains the photothermal capacity of AuNPs while enabling efficient excretion. Ultrasmall AuNPs (<6 nm) with a hydrophilic coating may be renally cleared, but clustering normally alters biodistribution and results in long-term hepatic deposition, prior to excretion. In this study, we employed p-methoxybenzenethiol (MBT) to stabilize ultrasmall AuNPs, clustered them with poly-(lactic-co-glycolic acid) (PLGA), and coated said clusters with acetylated human serum albumin (Ac-HSA). The resulting Ac-HSA-PLGA-AuNCs demonstrated reduced hepatic retention along with renal excretion of ultrasmall gold species. Intratumoral injection followed by 10 min NIR irradiation achieved efficient tumor ablation, outperforming conventional hyperthermia that usually requires hours of heating. Furthermore, combining Ac-HSA-PLGA-AuNC-based PTT with high-dose paclitaxel (160 mg/kg) was safe and yielded enhanced antitumor efficacy. This developed gold nanoplatform offers a promising strategy for translatable PTT-chemotherapy combinations.

Accurate characterisation of margins in excised breast cancer tumours is critical to the success of surgical interventions, yet margin status is typically confirmed post-operatively using histopathology. Here we present a new approach to intraoperative margin assessment based on microwave single pixel imaging, demonstrating tissue phantom hydration mapping across large areas ( 10 cm x 10 cm) at 1 mm resolution. By leveraging the photo-induced change in microwave transparency of a silicon modulator placed under the sample, we map the microwave reflectivity and identify positive margins with deeply sub-wavelength resolution. We test the discriminatory capabilities of our approach using gelatine-based tumour phantoms with variations in water density representative of the margin and cancerous tissues of a resected tumour. We demonstrate the capability to identify, locate and quantify inadequate margins up to the typically targeted minimum thickness of 2 mm. Furthermore, using numerical modelling, we show that our approach is expected to be resilient to patient-specific tissue differences. Our technique has potential for future deployment as a real-time intraoperative tissue margin analysis tool.

Single-grain pollen analysis is essential for advancing chemical palynology, which classifies morphologically similar taxa based on taxon-specific pollen and sporopollenin chemistry. Fourier-Transform Infrared (FT-IR) spectroscopy and microspectroscopy (µFT-IR) are widely used to differentiate morphologically similar grains but face limitations from Mie scattering and coarse spatial resolution. Here, we present the first application of Optical-PhotoThermal Infrared (O-PTIR) spectroscopy to chemically treated single pollen grains, using Molinia caerulea as a model taxon to fully test and evaluate the value of this new technique. O-PTIR achieves sub-micron “super-resolution” infrared spectroscopy through non-contact, non-destructive measurements by exploiting the photothermal effect. O-PTIR spectra exhibited close similarity to FT-IR datasets, with all major sporopollenin bands present and only minor peak shifts (4 cm-1). Use of spectra derived from sub-micron analyses for future classification purposes relies on a robust understanding of intra- and inter-grain variability. This can now be assessed using O-PTIR to distinguish whether intra-grain surface heterogeneity or true inter-grain biochemical differences drives grain separation. Multivariate analysis of intra- and inter-grain variability indicated that greater intra-grain variability does not drive greater inter-grain variability, confirming that observed pollen grain separation reflects true biochemical differences rather than technical or surface related effects. Similarly, intra- and inter-grain variability did not differ significantly between plants, indicating PCA clustering reflects subtle biochemical differences rather than significant plant-level variation. While a single O-PTIR measurement can produce a spectrum highly similar to the species centroid, reliable and representative results were reached when three to four measurements were averaged per grain.

In this work, we demonstrate the synthesis of gold nanoraspberries (AuNRB) using a HEPES buffer at room temperature. The study aimed to identify and compare the physicochemical conditions of the AuNRB and gold nanospheres (AuNS) of similar size to a selected set of reporter molecules. The dispersion stability of shape-controlled and AuNS of similar diameters was investigated in three different physiological media, ultrapure water, phosphate-buffered saline (PBS), and fetal bovine serum (FBS), and compared to understand the effect of NP shape, dispersion stability, and surface-enhanced Raman scattering (SERS) enhancement. We have used two nonresonant reporters, trans-1,2-bis(4-pyridyl) ethylene (BPE) and biphenyl-4-thiol (BPT), and a resonant reporter, IR820 (also known as new indocyanine green), a clinically approved dye for diagnostic studies, to explore the relative benefit of using molecular electronic resonance, i.e., comparing SERS vs surface-enhanced resonance Raman scattering (SERRS) with these nanoparticles. SERS has been explored extensively for biomedical applications, but the synthesis of bright gold nanoparticles and the appropriate Raman label is still challenging. To understand and optimize the SERS process, we have characterized both types of gold nanoparticles, ranging from their average size, zeta-potential, and ultraviolet-visible (UV-vis) absorption. It has been found that AuNRB and AuNS are most stable when dispersed in ultrapure water, while significant aggregation of both types has been observed when dispersed in PBS. With 10% FBS, there was a slight shift and increase in the surface plasmon absorbance peak, which resulted from an increase in particle size due to protein corona formation around the gold nanoparticles. For SERS efficiency, it has been found that AuNRB outperform AuNS with all reporters. Further, the resonant reporter, IR820, has provided a higher SERS signal compared to BPE and BPT and with its FDA approval for clinical use is clearly a strong candidate for future in vivo application.

Introduction:Gold nanoclusters (AuNCs) have emerged as promising agents for photothermal cancer therapy due to their unique optical properties and potential for tumour targeting. Methods:In this study, we developed clustered, excretable AuNCs using a glycol chitosan derivative (GCPQ) and investigated their physicochemical properties, photothermal effect, and therapeutic efficacy. Results:The AuNCs exhibited tunable surface plasmon resonance peaks dependent on the polymer:AuNP ratio, with optimized clusters showing surface enhanced Raman scattering and photothermal heating. In vivo studies in a mouse tumour model demonstrated significant tumour growth inhibition (334% growth vs 607% growth for untreated animals after 6 days) when combining intratumoural AuNC injection with near-infrared laser irradiation. Conclusion:The results provide proof-of-concept for the potential of these AuNCs in photothermal cancer therapy. Future work should focus on improving tumour targeting, optimizing treatment parameters, and assessing long-term safety to advance this platform toward clinical translation.

During post chemotherapy surgery for ovarian cancer, it is important to ensure that any residual disease is carefully assessed and removed. The assessment remains subjective, despite clear evidence of the benefits of complete macroscopic resection. In this work, we have considered Raman spectroscopy as a possible tool for residual disease assessment by exploring its ability to correctly classify ovarian cancer from benign and borderline tissues. Samples from seventy-three participants were analysed (n = 20 benign, n = 11 borderline and n = 42 cancer) using a multivariate analysis model. All models shown utilised validation with leave one participant out cross-validation. In ovarian tissue this model achieved 94% sensitivity and 98% specificity for prediction of cancer from benign and 98% sensitivity and 89% specificity for prediction of cancer from borderline. Thorough assessment of the surrounding peritoneal tissues is extremely important. For these peritoneal tissues taken from participants with advanced ovarian cancer, the model achieved 78% sensitivity and 84% specificity for prediction of cancerous peritoneum from benign peritoneum in participants who had primary surgery and 68% sensitivity and 81% specificity in participants who had post chemotherapy surgery. This demonstrates viability of Raman spectroscopy for assessment of ovarian cancer.

Many different types of nanoparticles have been developed for photothermal therapy (PTT), but directly comparing their efficacy as heaters and determining how they will perform when localized at depth in tissue remains complex. To choose the optimal nanoparticle for a desired hyperthermic therapy, it is vital to understand how efficiently different nanoparticles extinguish laser light and convert that energy to heat. In this paper, we apply photothermal mass conversion efficiency (eta m ) as a metric to compare nanoparticles of different shapes, sizes, and conversion efficiencies. We selected silica-gold nanoshells (AuNShells), gold nanorods (AuNRs), and gold nanostars (AuNStars) as three archetypal nanoparticles for PTT and measured the eta m of each to demonstrate the importance of considering both photothermal efficiency and extinction cross section when comparing nanoparticles. By utilizing a Monte Carlo model, we further applied eta m to model how AuNRs performed when located at tissue depths of 0-30 mm by simulating the depth penetration of near-infrared (NIR) laser light. These results show how nanoparticle concentration, laser power, and tissue depth influence the ramp time to a hyperthermic temperature of 43 degrees C. The methodology outlined in this paper creates a framework to benchmark the heating efficacy of different nanoparticle types and a means of estimating the feasibility of nanoparticle-mediated PTT at depth in the NIR window. These are key considerations when predicting the potential clinical impact in the early stages of nanoparticle design.

X-ray diffraction is widely used to characterise the mineral component of calcified tissue. Broadening of the diffraction peaks yields valuable information on the size of coherently diffracting domains, sometimes loosely described as crystallite size or crystallinity. These domains are markedly anisotropic, hence a single number describing their size is misleading. We present a novel variation on a method for visualising crystallographic anisotropy in X-ray diffraction data. This provides an intuitively interpretable depiction of crystalline domain size and anisotropy. The new method involves creating a polar plot of calculated domain thickness for peaks in a diffractogram versus crystallographic direction. Points with the least error are emphasised. Anisotropic domain dimensions are calculated by refining an ellipsoidal model in a whole pattern fit. These dimensions are then used to overlay an ellipse on the peak broadening plot. This is illustrated by application of the method to calcifications in breast tissue with suspected cancer, which frequently contain whitlockite as well as nanocrystalline apatite. Like most biogenic apatite, this exhibits markedly anisotropic peak broadening. The nature of this anisotropy offers potentially useful information on normal function and pathology of calcified tissue and is a frequently neglected crystallographic feature of these materials.

Raman spectroscopy provides comprehensive biochemical information on a sample's composition, yet it is often used to analyze aggregated spectra rather than specific shifts. We introduce Fluorescence Guided Raman Spectroscopy (FGRS) as a methodology enabling the isolation of proteins' spectral signatures and the training of classifiers that generalize across cell lines. We demonstrate the utility of this approach using connexin 43, a marker protein of glioblastoma tumour microtubes. By screening eGFP, sodium fluorescein, and mTagBFP2 for their compatibility with a Raman system operating at 532 nm, we selected mTagBFP2 as the most Raman-compatible fluorophore, whereas the other fluorophores emitting near 532 nm caused spectral interference. mTagBFP2 was cloned into a connexin 43 expression vector, allowing fluorescent tracking and Raman interrogation with subsequent peak identification and correlation to an I-TASSER protein prediction model. We then trained two support vector machines (SVMs) for the classification of cells based on their connexin 43 content and highlighted the impact of different spectral ranges (full spectrum vs. most significant Raman shifts) on specificity and sensitivity in glioblastoma target cell lines. Connexin 43 expression led to a loss of the peaks at 600, 1253, and 1401 cm⁻¹, consistent with an increased α-helical content as predicted by I-TASSER. SVMs achieved up to 79% accuracy on unseen glioblastoma lines, with full-spectrum models reaching 98.7% sensitivity. Thus, FGRS enables the spectral isolation of tumour marker proteins and the development of robust classifiers across cell lines. By focusing on key Raman shifts, this method holds the potential to improve diagnostic accuracy and sensitivity, offering a customizable tool for tumour detection.

Ductal carcinoma in situ (DCIS) is a potential precursor to invasive breast cancer (IBC). The trajectory of an individual’s DCIS, if it will progress to IBC or remain as DCIS is difficult to predict. Currently >80% of DCIS is detected through mammographic screening of breast calcifications. Despite the close association of calcifications with DCIS, their role in the development of DCIS and/or its progression to IBC remains largely unexplored. In this study, we present a spectroscopy based analytical approach to probe chemical compositional changes of both DCIS associated breast calcifications and surrounding soft tissue aiming to identify a cohort at increased risk for invasive progression. Tissue samples from 316 DCIS patients without invasive cancer were obtained from multiple centres as part of the PRECISION consortium (The Netherlands, UK and USA). Three consecutive tissue sections were obtained each of the tissue biopsies. Mid-infrared (mid-IR) and Raman hyperspectral imaging was performed independently on two sections that were left unstained. The third H&E-stained section was used to annotate calcifications and histopathological features. All DCIS samples had known outcome (i) ‘pure DCIS as controls’ (DCIS without progression to invasion) (n=193), (ii) ‘DCIS with progression to invasion as cases’ (DCIS from patients who subsequently developed invasive disease after initial treatment) (n=123). Spectral features of DCIS calcifications and surrounding soft tissue were used as inputs for analysis. Data was divided into a discovery and a validation set. Cluster analysis followed by Principal component analysis fed linear discriminant analysis was carried out on the discovery set to develop DCIS prediction models. For the Raman data, a mean area under the receiver operating characteristic curve (AUROC) value of 0.85 was obtained using calcification spectral features, and 0.75 using soft tissue spectral features in distinguishing controls from cases (N=118 vs 52). Similar analysis on the IR data showed a mean AUROC value of 0.68 for calcification, 0.78 for epithelial and 0.80 for stromal components (N=97 vs 61). Preliminary analysis shows changes in phosphate to carbonate ratio and variations in magnesium whitlockite content in calcifications, and protein secondary structural changes in soft tissue, between the two groups. The models will be tested independently on the validation set and the outcomes will be presented at the AACR conference. Spectroscopic chemical analysis of breast calcifications and soft tissue show promise in predicting the likely progression of DCIS to IBC. Pending further independent validation, these techniques appear to be novel image-based risk assessment tools that can potentially be utilised to inform DCIS prognosis and treatment options. Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Esther Lips, Ihssane Bouybayoune, Rachel Factor, Sarah Pinder, Lorraine King, Jeffrey Marks, Thomas Lynch, Donna Pinto, Alastair Thompson, Elinor Sawyer, Jelle Wesseling, Shelley Hwang, Keith Rogers, Nick Stone, Grand Challenge PRECISION consortium. Predicting the prognosis of ductal carcinoma in situ through chemical analysis of breast microcalcifications and soft tissue using infrared and Raman spectroscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3353.

Calcifications across the body offer snapshots of the surrounding ionic environment at the time of their formation. Links between prostate calcification chemistry and cancer are becoming of increasing interest, particularly in identifying biomarkers for disease. This study utilizes X-ray fluorescence mapping of 72 human prostate calcifications, measured at the I18 beamline at the Diamond Light Source, to determine the links between calcifications and their environment. This paper offers the first investigation of the elemental heterogeneity of prostate calcifications, demonstrating lower relative levels of minor elements at the calcification center compared to the edge but higher levels of zinc. Importantly, this study uniquely presents links between average Fe, Cr, Mn, Cu, and Ni ratios and grade Group (a classification system for urological tumors, specifically for prostate cancer), highlighting a potential avenue of exploration for biomarkers in prostate calcifications.

Background: Benign breast disease (BBD) is commonly detected in women participating in breast cancer screening programs and comprises a diverse group of lesions. The clinical significance of BBD lies in its association with an increased risk of developing breast cancer, which depends on the histological subtype. Calcifications, frequently observed in mammographic screenings, are critical in the detection and diagnosis of both benign and malignant breast conditions. While many calcifications are benign, some patterns are indicative of ductal carcinoma in situ (DCIS) or invasive breast cancer (IBC). Better characterization of breast cancer risk among women with BBD, considering both clinical and molecular findings, can improve surveillance, early diagnosis, and survival. This study aims to identify mammographic and chemical characteristics of calcifications that are associated with subsequent development of DCIS or IBC. Methods: A matched case-control study was conducted of women diagnosed with BBD at the Netherlands Cancer Institute and Albert Schweitzer Hospital between 2004 and 2023. Cases (n=65) were women with BBD who developed ipsilateral DCIS or IBC ≥ 6 months after a first BBD diagnosis, whereas controls (n=244) were BBD patients who did not develop subsequent ipsilateral DCIS or IBC during the follow-up (FU) duration of their matching cases. Additionally, controls were matched based on the year and age at the time of BBD diagnosis. Patient characteristics (e.g. age at diagnosis, vital status) and characteristics of both the benign lesions and subsequent malignant lesions were extracted from pathology reports using text searches and Palga codes. Mammographic lesion types (e.g. calcifications, masses, architectural distortion, asymmetries) were extracted from radiology reports. Qualitative mammographic features including calcification morphology and distribution were extracted from mammograms by two researchers and a trained radiologist. Quantitative mammographic features including breast density score and calcification cluster size and number will be extracted using TRANSPARA 2.0, an radiology artificial intelligence decision support system. In a subset of cases (n = 29) and controls (n=59) chemical characteristics were measured using infrared and Raman spectroscopy. Results: The baseline comparison of mammographic qualitative features comprised 65 cases and 244 matched controls.Median age at BBD diagnosis was 51 years (range 35-80). Median FU from BBD diagnosis to DCIS or IBC was 6 years (range 1-17). Most cases and controls had non-proliferative BBD (89% and 94%) rather than proliferative BBD. Among cases, 19 developed DCIS while 46 developed IBC. While cases and controls showed comparable proportions of mammographic lesions, calcifications were more prevalent among cases (48.0% vs. 34.0%), approaching statistical significance (p = 0.06). Significant differences in calcification morphology were observed (p = 0.009), with cases more likely to display fine pleomorphic calcifications (23 % vs. 8.5%). The distribution of calcifications was similar between cases and controls (p = 0.49). Multivariate-adjusted conditional regression models showed an odds ratio (OR) of 1.7 (95% CI: 0.9-3.0) for the association between calcification presence and DCIS/IBC development, albeit with considerable uncertainty. Presence of suspicious calcification morphologies (amorphous, fine pleomorphic, linear) suggested an OR of 3.0 (95% CI: 0.9-10.0) compared to benign morphology. Conclusions: The trends observed in this study suggest potential prognostic value of calcification morphology in women with BBD. Additional results on quantitative mammographic features and chemical characteristics of cases and controls will be presented at the conference. Citation Format: Merle van Leeuwen, Sandra van den Belt-Dusebout, Jia Ning Zhuchen, Shannon Doyle, Petra Kristel, Lennart Mulder, Jayakrupakar Nallala, Pieter Westenend, Nick Stone, Esther Lips, Ritse Mann, Jelle Wesseling. Calcification characteristics in women with benign breast disease and the risk of subsequent breast cancer: a case-control study [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-03-29.

Predicting long-term recurrence of disease in breast cancer (BC) patients remains a significant challenge for patients with early stage disease who are at low to intermediate risk of relapse as determined using current clinical tools. Prognostic assays which utilize bulk transcriptomics ignore the spatial context of the cellular material and are, therefore, of limited value in the development of mechanistic models. In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. A number of deep learning models were employed, with champion models employing two-dimensional and two-dimensional-separable convolutional networks found to have predictive performance of a ROC AUC of approximately 0.64, which compares well to other clinically used prognostic assays in this space. All-digital chemical imaging may therefore provide a label-free platform for histopathological prognosis in breast cancer, opening new horizons for future deployment of these technologies.

We designed and optimized a dual-functional photothermal agent that performs as a nanoheater and real-time optical thermometer by leveraging gold nanoparticle (AuNP) self-assembly and anti-Stokes thermometry. We engineered colloidally stable fractal AuNP clusters with well-defined nanogaps to absorb strongly in the near-infrared and enhance anti-Stokes vibrational modes via surface-enhanced Raman scattering (SERS) for electromagnetic (EM) hotspot-localized thermometry during plasmonic heating. Photothermal characterization and simulations of a range of AuNP building block sizes demonstrated that 40 nm AuNPs are optimum for combined plasmonic heating and SERS due to the high probability of in resonance chains within assemblies. We explored the relationship between the far-field of our AuNP clusters and the near-field enhancement of anti-Stokes modes in the context of SERS thermometry, setting out design considerations for applying SERS thermometry. Finally, using a single near-infrared (NIR) laser source, we demonstrated plasmonic heating of a colloidal system with simultaneous accurate temperature measurement from EM hotspots via the thermal information encoded in the anti-Stokes mode of surface-bound Raman reporter molecules. Ultimately, our approach could enable real-time noninvasive temperature feedback from plasmonic nanoparticles within tumor tissue environments to guide safe and effective temperature increases during cancer photothermal therapy.

Prostate cancer remains the most common male cancer; however, treatment regimens remain unclear in some cases due to a lack of agreement in current testing methods. Therefore, there is an increasing need to identify novel biomarkers to better counsel patients about their treatment options. Microcalcifications offer one such avenue of exploration. Microfocus spectroscopy at the i18 beamline at Diamond Light Source was utilised to measure X-ray diffraction and fluorescence maps of calcifications in 10 µm thick formalin fixed paraffin embedded prostate sections. Calcifications predominantly consisted of hydroxyapatite (HAP) and whitlockite (WH). Kendall’s Tau statistics showed weak correlations of ‘a’ and ‘c’ lattice parameters in HAP with GG (rτ = − 0.323, p = 3.43 × 10–4 and rτ = 0.227, p = 0.011 respectively), and a negative correlation of relative zinc levels in soft tissue (rτ = − 0.240, p = 0.022) with GG. Negative correlations of the HAP ‘a’ axis (rτ = − 0.284, p = 2.17 × 10–3) and WH ‘c’ axis (rτ = − 0.543, p = 2.83 × 10–4) with pathological stage were also demonstrated. Prostate calcification chemistry has been revealed for the first time to correlate with clinical markers, highlighting the potential of calcifications as biomarkers of prostate cancer.

Autologous photoreceptor cell replacement is one of the most promising strategies currently being developed for the treatment of patients with inherited retinal degenerative blindness. Induced pluripotent stem cell-derived (iPSC-derived) retinal organoids, which faithfully recapitulate the structure of the neural retina, are an ideal source of transplantable photoreceptors required for these therapies. However, retinal organoids contain other retinal cell types, including bipolar, horizontal, and amacrine cells, which are unneeded and may reduce the potency of the final therapeutic product. Therefore, approaches for isolating fate-committed photoreceptor cells from dissociated retinal organoids are desirable. In this work, we present partial dissociation, a technique that leverages the high level of organization found in retinal organoids to enable selective enrichment of photoreceptor cells without the use of specialized equipment or reagents such as antibody labels. We demonstrate up to 90% photoreceptor cell purity by simply selecting cell fractions liberated from retinal organoids during enzymatic digestion in the absence of mechanical dissociation. Since the presented approach relies on the use of standard plasticware and commercially available current good manufacturing practice-compliant reagents, we believe that it is ideal for use in the preparation of clinical photoreceptor cell replacement therapies.

The article provides a perspective overview of the development and current status of the field of biomedical spectroscopy, on the occasion of the tenth anniversary of the foundation of the International Society for Clinical Spectroscopy (CLIRSPEC). It provides a background into the prior evolution of the field, and international community, and rationale for the foundation of the legal entity, CLIRSPEC. It describes the continued dissemination and educational activities associated with the society, and maps out key technical developments, as well as trends towards clinical translation, and future perspectives.

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Background Patients diagnosed with ductal carcinoma in situ (DCIS) may also have undetected invasive breast cancer. Radiomic features of calcifications at mammography can predict occult invasive disease among women diagnosed with DCIS at core-needle biopsy, which could affect treatment recommendations. However, the generalizability of these radiomic models must be tested before they are adopted in clinical practice. Purpose To evaluate the generalizability of radiomic models based on mammography features to predict occult invasive cancer among women diagnosed with DCIS at core-needle biopsy from three national datasets. Materials and Methods In this retrospective, cross-national study, digital mammograms from women diagnosed with DCIS at breast core-needle biopsy were collected in the United States, United Kingdom, and the Netherlands between January 1, 2000, and December 31, 2021. Only asymptomatic women who had calcifications but did not have associated masses, architectural distortions, or asymmetries were included. Radiomic models were developed using cross-validated logistic regression on each national dataset, then round-robin tested on the other datasets. Differences across the three datasets in terms of the upstaging rate, age, lesion size, and estrogen and progesterone receptor levels were assessed using Kruskal-Wallis or χ2 test. Results The study included 1498 women (age range, 31-89 years; mean age, 59 years ± 9 [SD]), as follows: 696 women from the United States, 618 women from the United Kingdom, and 184 women from the Netherlands, with upstaging rates of 16.1%, 16.7%, and 14.1%, respectively. Internal cross-validation areas under the receiver operating characteristic curve (AUCs) were 0.675 (95% CI: 0.671, 0.679), 0.603 (95% CI: 0.567, 0.722), and 0.701 (95% CI: 0.697, 0.706) for the U.S., UK, and Netherlands datasets, respectively. The model that was trained on the U.S. dataset yielded cross-national validation AUCs of 0.604 (95% CI: 0.560, 0.648) and 0.682 (95% CI: 0.607, 0.757) for the UK and Netherlands datasets. Conclusion Radiomic machine learning models were shown to have the potential to predict occult invasive cancer in women with DCIS across diverse settings. © RSNA, 2025 Supplemental material is available for this article.