Journal of Biophotonics

Toward noncontact macroscopic imaging of multiple cancers using multi‐spectral inelastic scattering detection

Abstract Here we introduce a Raman spectroscopy approach combining multi‐spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field‐of‐view of 1‐centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity – in brain, breast, lung, and ovarian/endometrium tissue – using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof‐of‐principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band – here the CH 2 /CH 3 deformation bands from proteins and lipids – using a conventional multi‐spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label‐free, and wide field.

Label‐free detection of living cervical cells based on microfluidic device with terahertz spectroscopy

AbstractEarly diagnosis of cervical cancer is essential for a good prognosis. Terahertz wave detection technology is a nondestructive and label‐free physical detection technology, which can detect and monitor the cancer cells in real time, especially for patients with deep or inaccessible tumors. In this study, a single‐cell‐layer microfluidic device was developed. After replacing the optical clearing agent, the characteristics of H8, HeLa and SiHa cell lines in adherent and suspended states were detected. Additionally, the absorption increased with increasing cell density. For the mixed suspension cell samples, principal component analysis–support vector machine method was used to identify benign and malignant cell component. After living cells formaldehyde, changes in cell membrane permeability were evaluated to identify the cell survival status (i.e., dead or living) based on terahertz spectroscopy amplitude differences. Therefore, extending the terahertz spectrum detection to the molecular level can characterize the life essence of cells and tissues.

Development of highly reliable SERS‐active photonic crystal fiber probe and its application in the detection of ovarian cancer biomarker in cyst fluid

AbstractConventionally Surface‐enhanced Raman spectroscopy (SERS) is realized by adsorbing analytes onto nano‐roughened planar substrate coated with noble metals (silver or gold) or their colloidal nanoparticles (NPs). Nanoscale irregularities in such substrates/NPs could lead to SERS sensors with poor reproducibility and repeatability. Herein, we demonstrate a suspended core photonic crystal fiber (PCF) based SERS sensor with extremely high reproducibility and repeatability in measurement with a relative SD of only 1.5% and 4.6%, respectively, which makes it more reliable than any existing SERS sensor platforms. In addition, our platform could improve the detection sensitivity owing to the increased interaction area between the guided light and the analyte, which is incorporated into the holes that runs along the length of the PCF. Numerical calculation established the significance of the interplay between light coupling efficiency and evanescent field distribution, which could eventually determine the sensitivity and reliability of the developed SERS active‐PCF sensor. As a proof of concept, using this sensor, we demonstrated the detection of haptoglobin, a biomarker for ovarian cancer, contained within the ovarian cyst fluid, which facilitated in differentiating the stages of cancer. We envision that with necessary refinements, this platform could potentially be translated as a next‐generation highly sensitive SERS‐active opto‐fluidic biopsy needle for the detection of biomarkers in body fluids.

Label‐free assessment of the transformation zone using multispectral diffuse optical imaging toward early detection of cervical cancer

Abstract The assessment of the transformation zone is a critical step toward diagnosis of cervical cancer. This work involves the development of a portable, label‐free transvaginal multispectral diffuse optical imaging (MDOI) imaging probe to estimate the transformation zone. The images were acquired from N  = 5 ( N  = 1 normal, N  = 2 premalignant, and N  = 2 malignant) patients. Key parameters such as spectral contrast ratio ( ρ ) at 545 and 450 nm were higher in premalignant (0.29, 0.25 for 450 nm and 0.30, 0.17 for 545 nm) as compared to the normal patients (0.13 and 0.14 for 450 and 545 nm, respectively). The threshold for the spectral intensity ratio R610/R450 and R610/R545 can also be used as a marker to correlate with the new and original squamous columnar junction (SCJ), respectively. The pilot study highlights the use of new markers such as spectral contrast ratio ( ρ ) and spectral intensity ratio (R610/R450 and R610/R545) images.

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi‐spectral photoacoustic tomography

AbstractIn photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi‐pixel method to smooth the measurements before calculating rHbT and %sO2. We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co‐registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90‐0.96) on the testing data set was achieved by combining mean %sO2, a spectral feature, and the score of the study radiologist.

Detection of living cervical cancer cells by transient terahertz spectroscopy

AbstractThe photonic energy of terahertz wave is in the same order of magnitude as the rotational and vibrational energy levels of organic and biological macromolecules, so it has unique advantages in detecting cells and biological macromolecules. However, in the life environment, the dynamic time scale of cell‐environment interaction and structural conformation change of biological macromolecules are within picosecond to millisecond, and water has strong absorption to terahertz wave, which has become the bottleneck problem for the detection of cells and biological macromolecules by terahertz technology. In this article, we developed a set of terahertz single measurement system based on the tilt wave front of grating pulse technique. The system was employed for the terahertz detection of trace living cervical cancer cells. We achieved transient detection of the terahertz pulse time‐domain waveform of the living HeLa cells. The characteristic absorption peaks were identified by Lambert‐Beer law, respectively, at 0.49, 0.71, 1.04, 1.07, 1.26 and 1.37 THz. The absorbance is proportional to the cell concentration.

Spatial autocorrelation analysis on two‐dimensional images of Mueller matrix for diagnosis and differentiation of cervical precancer

AbstractThe spatial autocorrelation and correlation map of amplitude and phase anisotropy along with depolarization parameter from the stroma of uterine cervix utilizing their Mueller matrix (MM) images have been reported for early diagnosis of cervical cancer and differentiation of precancerous stages. The comparative results of the evaluation of the spatial autocorrelation over MM images of optically anisotropic collagen structures from normal and various grades of cervical precancer reflect significant alterations which are correlated with the pathological changes. The spatially varying polarizance from different region of anisotropic stromal region gets correlated within a given spatial lag during the precancerous changes. The diattenuation governing elements M12, M13 and M14 clearly discriminate normal and various grades of precancerous cervical tissue through their autocorrelation profile and correlation map. Evaluation of autocorrelation of spatially varying linear birefringence and linear‐45 birefringence characterized by MM elements M34 and M43 and M24 and M42 are not found to differ between the precancer grades, indicating that these changes may be arising from highly directional collagen network while the changes displayed by MM elements M23 and M32 faithfully represent that the chirality of the stromal region is compromised as the cervical cancer evolves and only one type of nature dominates.

Machine Learning‐Based Thermal Imaging for Vulvar Intraepithelial Neoplasia Detection

ABSTRACT Vulvar intraepithelial neoplasia (VIN) is increasing in prevalence, yet screening options remain limited and existing diagnostic methods show low accuracy. This study evaluates infrared thermal imaging as an alternative screening approach for VIN detection. We analyzed thermal images from 51 patients with histopathologically confirmed VIN, captured using a FLIR A400 thermal camera. Temperature distributions of healthy vulvar tissue were first characterized to establish baseline values. Thermal features of VIN lesions were then extracted and optimized using principal component analysis (PCA). Three machine learning models—support vector machine (SVM), random forest (RF), and linear discriminant analysis (LDA)—were trained and evaluated for VIN diagnosis. SVM demonstrated the best performance with an F 1 score of 75% and accuracy of 74.19%. These findings suggest that machine learning‐based infrared thermography shows promise as a non‐invasive screening tool for VIN detection.

Near‐infrared emissive polymer‐coated IR ‐820 nanoparticles assisted photothermal therapy for cervical cancer cells

Abstract Photothermal therapy (PTT) has attracted wide attention due to its noninvasiveness and its thermal ablation ability. As photothermal agents are crucial factor in PTT, those with the characteristics of biocompatibility, non‐toxicity and high photothermal stability have attracted great interest. In this work, new indocyanine green (IR‐820) was utilized as a photothermal agent and near‐infrared (NIR) fluorescence imaging nanoprobe. To improve the biocompatibility, poly(styrene‐co‐maleic anhydride) (PSMA) was utilized to encapsulate the IR‐820 molecules to form novel IR‐820@PSMA nanoparticles (NPs). Then, the optical and thermal properties of IR‐820@PSMA NPs were studied in detail. The IR‐820@PSMA NPs showed excellent photothermal stability and biocompatibility. The cellular uptaking ability of the IR‐820@PSMA NPs was further confirmed in HeLa cells by the NIR fluorescent confocal microscopic imaging technique. The IR‐820@PSMA NPs assisted PTT of living HeLa cells was conducted under 793 nm laser excitation, and a high PTT efficiency of 73.3% was obtained.

Automated thermal imaging monitors the local response to cervical cancer brachytherapy

AbstractMalignant tumors have high metabolic and perfusion rates, which result in a unique temperature distribution as compared to healthy tissues. Here, we sought to characterize the thermal response of the cervix following brachytherapy in women with advanced cervical carcinoma. Six patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7‐day follow‐up period. A designated algorithm was used to calculate and store the texture parameters of the examined tissues across all time points. We used supervised machine learning classification methods (K Nearest Neighbors and Support Vector Machine) and unsupervised machine learning classification (K‐means). Our algorithms demonstrated a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy. Thus, we showed that thermal imaging combined with advanced feature extraction could potentially be used to detect tissue‐specific changes in the cervix in response to local brachytherapy for cervical cancer.

Evaluating the Efficacy of Mebendazole Repurposing for Ovarian Cancer Therapy Using Optical Coherence Tomography

ABSTRACT Ovarian cancer (OvCa) remains the leading cause of gynecological cancer mortality, with most patients developing chemoresistance. Drug repurposing offers promising alternatives, with mebendazole (MBZ) showing anticancer activity. This study evaluates MBZ efficacy using Spectral Domain Optical Coherence Tomography (SD‐OCT). We conducted longitudinal imaging of 40 wild‐type (WT) and cisplatin‐resistant (CPR) OVCAR8 multicellular tumor spheroids over 11 days. Four analyses were performed: volume analysis, optical attenuation analysis, uniformity analysis, and texture feature analysis. Volume analysis showed MBZ reduced spheroid growth in both groups, with greater effects in CPR‐MCTs. Optical attenuation analysis revealed increased necrotic tissue ratios in treated spheroids. Uniformity analysis demonstrated MBZ targets heterogeneous tissues effectively. Texture analysis identified significant structural changes, with 866 altered features in CPR spheroids versus 124 in WT spheroids. Cell viability assays confirmed MBZ's effectiveness against standard and chemo‐resistant OVCAR8 tumors. This study demonstrates SD‐OCT's utility for noninvasive therapy monitoring in 3D cancer models.

DeepLabV3+ With Convolutional Triplet Attention and Histopathology‐Guided Voting for Hyperspectral Image Segmentation of Serous Ovarian Cancer

ABSTRACTDeep learning has been extensively applied in medical image analysis, providing healthcare professionals with more efficient and accurate diagnostic information. Among these advanced semantic segmentation models, the baseline DeepLabV3+ model is more adept at processing low‐dimensional data such as RGB images, but its performance on high‐dimensional data like hyperspectral images is suboptimal, limiting its generalization and discriminative capabilities. We propose a highly innovative hybrid architecture integrating a Convolutional Triplet Attention Module (CTAM) to capture cross‐dimensional spectral‐spatial dependencies and a Histopathology‐Guided Voting Mechanism (HVM) to incorporate WHO diagnostic criteria. The results demonstrate that our model can accurately differentiate and localize low‐grade and high‐grade serous ovarian cancer tissues, with an accuracy of 92.7% and 90.2%, respectively. Furthermore, our performance exceeds the pathologist's consensus (85.4%) and surpasses state‐of‐the‐art models (e.g., U‐Net, PAN, FPN) by a significant margin of over 20% in LGSC classification, rigorously validating its scientific superiority.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging

Abstract Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer‐related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label‐free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

Label‐free imaging diagnosis and collagen‐optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy

AbstractThe assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two‐photon excited fluorescence intensity. Moreover, by utilizing semi‐automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

Multispectral label‐free Raman spectroscopy can detect ovarian and endometrial cancer with high accuracy

AbstractUp to 70% of ovarian cancer patients are diagnosed with advanced‐stage disease and the degree of cytoreduction is an important survival prognostic factor. The aim of this study was to evaluate if Raman spectroscopy could detect cancer from different organs within the abdominopelvic region, including the ovaries. A Raman spectroscopy probe was used to interrogate specimens from a cohort of nine patients undergoing cytoreductive surgery, including four ovarian cancer patients and three patients with endometrial cancer. A feature‐selection algorithm was developed to determine which spectral bands contributed to cancer detection and a machine‐learning model was trained. The model could detect cancer using only eight spectral bands. The receiver‐operating‐characteristic curve had an area‐under‐the‐curve of 0.96, corresponding to an accuracy, a sensitivity and a specificity of 90%, 93% and 88%, respectively. These results provide evidence multispectral Raman spectroscopy could be developed to detect ovarian cancer intraoperatively.

Monitoring the extracellular matrix remodeling of high‐grade serous ovarian cancer with nonlinear optical microscopy

AbstractThe mortality of high‐grade serous ovarian cancer (HGSOC) accounts for 70% to 80% of all ovarian cancer deaths and overall mortality rate has not declined in the last decade. Recently, many studies have demonstrated that HGSOC originates from the fallopian tubes. The extracellular matrix (ECM) is present in all tissues, its remodeling and interaction with cells are crucial for regulating cell proliferation, migration, and differentiation. In this paper, we used label‐free nonlinear optical microscopy to image tissues of the fallopian tube and ovary. Combining a set of image processing algorithms, we monitored the remodeling of ECM in the fallopian tube and ovary during the invasion of primary serous fallopian tube tumor into the ovary in microscopic dimension. With this approach, we can obtain physiological information of HGSOC at the early stage, which provided useful data for auxiliary clinical diagnosis.

Empirical Mode Decomposition and Grassmann Manifold‐Based Cervical Cancer Detection

ABSTRACTCervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi‐feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non‐linear subspace structure within spectral signals, which is subsequently followed by a low‐rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five‐fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state‐of‐the‐art machine learning classifiers were also used and compared with the RF model.

Hyperspectral Imaging of Uterine Fibroids

ABSTRACTUterine fibroids are non‐cancerous growths of the uterus that affect nearly 70%–80% of women in their lifetimes. Fibroids can cause severe pain, bleeding, and infertility. The main risk of recurrence is smaller fibroids, which are notoriously hard to detect, being missed during a surgical removal procedure, only to enlarge afterwards. In this work, hyperspectral imaging (HSI) datasets were acquired from samples from 10 patients after receiving a hysterectomy. Optical properties including absorption, scattering, and spectral morphology were extracted and fed into machine learning to classify regions as fibroid and myometrium. Top extracted optical features had significant contrast between fibroid and myometrium (p < 0.0001) and were used to train Random Forest (AUC: 0.9985 ± 0.001, Sensitivity: 0.9534 ± 0.019, Specificity: 0.9936 ± 0.009) and Logistic Regression (AUC: 0.9397 ± 0.013, Sensitivity: 0.8405 ± 0.023, Specificity: 0.8895 ± 0.032) with strong performance across testing splits. With HSI, there is contrast between fibroid and myometrium in the human uterus.

In Vivo Cervical Precancer Classification Through Multifractal Analysis of Spectral Fluctuations in Intrinsic Fluorescence Spectra

ABSTRACT Spectral fluctuations in fluorescence spectroscopy, often ignored as noise, contain significant information about the fluorophore microenvironments. We present a discrete wavelet transform (DWT)‐based technique to extract spectral fluctuations from the intrinsic fluorescence signals and utilize them to classify normal and precancerous patients. The fluctuations are extracted by applying the inverse DWT after zeroing the approximation and noisy detail coefficients. Multifractal detrended fluctuation analysis revealed stronger multifractality for precancer signals manifested in the singularity spectrum. The Hurst exponent () and the Hausdorff dimension clearly distinguish two groups. Random Forest classification of generalized Hurst and Holder exponents achieves 96% sensitivity, specificity, and accuracy with an AUC of 0.98. This indicates that the spectral fluctuations derived from the intrinsic fluorescence data capture the subtle, distinctive features, resulting in better classification between the two grades. Further, a comparison among various mother wavelet functions reveals the best performance for the “bior2.4” wavelet.

Near‐Infrared Spectroscopy Mapping for Uterine Cancer and Fibroid Detection

ABSTRACTEndometrial cancer and uterine leiomyomas (fibroid) are common uterine pathologies that require early diagnosis to improve a patient's symptoms and increase the success rate of interventional procedures. In this work, we report on near‐infrared spectroscopy (NIRS) spectral features of uterine cancer and fibroids from 69 surgical specimens obtained from 24 patients following hysterectomies. Normal uterus, cancer, and fibroid tissue were identified by NIR spectral contrast parameters based on the differences in spectrum morphology. Using the significant optical features and spectral principal components, a classification model was able to classify uterus tissue with a prediction accuracy higher than 70%, identifying cancer specimens with 70% sensitivity and 93% specificity, and fibroid samples with 86% sensitivity and 83% specificity. These results demonstrated NIRS mapping has promise as a complementary method for gynecologic imaging.

Clinical Effect Analysis and Prognostic Factors of Photodynamic Therapy for Cervical Precancerous Lesions

ABSTRACTObjectiveTo evaluate the therapeutic effect of photodynamic therapy (PDT) on cervical precancerous lesions and prognostic factors.Methods173 patients diagnosed with cervical precancerous lesions and treated with PDT were included. Univariate analysis was performed on human papillomavirus (HPV) infection, patient age, number of treatments and number of medications before and after treatment, to evaluate the efficacy, adverse reactions and patient satisfaction.Results112 patients (64.5%) showed HPV clearance afrer PDT. Age ≥ 40 years was positively correlated with poor prognosis and number of treatments ≥ 6 was negatively correlated with poor prognosis. The satisfaction rate reached 94.79%, with 6 patients experiencing tolerable pain and no treatment discontinuations due to adverse reactions.ConclusionPDT is effective and safe for treating cervical precancerous lesions, with patient age and treatment frequency impacting outcomes. High patient satisfaction and tolerable adverse reactions support the application of PDT in clinical practice.

Spatially Resolved Fibre‐Optic Probe for Cervical Precancer Detection Using Fluorescence Spectroscopy and PCA‐ANN‐Based Classification Algorithm: An In Vitro Study

ABSTRACTCervical cancer can be detected at an early stage through the changes occurring in biochemical and morphological properties of epithelium layer. Fluorescence spectroscopy has the ability to identify these subtle changes non‐invasively and in real time with good accuracy in comparison with conventional techniques. In this paper, we report the usage of a custom designed spatially resolved fibre‐optic probe (SRFOP), which consists of 77 fibres in two concentric rings, for the detection of cervical cancer using fluorescence spectroscopy technique. The aim of this study is to classify different grades of cervical precancer on the basis of their fluorescence spectra followed by a robust classification algorithm. Fluorescence spectra of 28 cervical tissue samples of different categories have been recorded using six detector fibres of FOP at different spatial locations with the source fibre (SF). A 405 nm laser diode source has been utilised to excite the samples and a USB 4000 Ocean Optics spectrometer to collect the output spectra in the wavelength range 400–700 nm. Principal component analysis (PCA) was applied to the collected spectra to reduce the dimensionality of the data while preserving the most significant features for classification. The first 10 principal components, which captured the majority of the variance in the spectra, were selected as input features for the classification model. Classification was then performed using an artificial neural network (ANN) with a specific architecture, including an input layer, hidden layers, and a softmax activation function in the output layer. Experimental and classification results both demonstrate that proximal fibres (PFs) perform better than distal fibres (DFs) in capturing the discriminatory features present in the epithelium layer of cervical tissue samples as PF collect most of the signal from the epithelium layer. The combined approach of spatially resolved fluorescence spectroscopy and PCA‐ANN classification techniques is able to discriminate different grades of cervical precancer and normal with an average sensitivity, specificity and accuracy of 93.33%, 96.67% and 95.57%, respectively.

A smartphone‐based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions

AbstractReal‐time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone‐based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short‐term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10‐fold cross‐validation. This exploratory study demonstrates the potential of combining smartphone‐based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

Depth‐resolved attenuation mapping of the human ovary and fallopian tube using optical coherence tomography

AbstractDue to the lack of reliable early‐diagnostic tools, most ovarian cancers are diagnosed at late stages. Although optical coherence tomography (OCT) has shown promise for identifying diseased ovaries and fallopian tubes at an earlier stage, previous studies either did not provide quantitative scattering mapping or simply used Beer's law to fit the scattering coefficients of each A‐line. In this paper, we calculated the pixel‐wise attenuation coefficients of ovaries and fallopian tubes in OCT images. Data from 73 freshly excised human ovaries and fallopian tubes from 36 patients have shown that statistical features are statistically different between cancerous ovaries, infundibula, and fimbriae and normal ones.

Cervical pre‐cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe

AbstractCervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in‐vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in‐house developed handheld probe (i‐HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10‐fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i‐HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics.

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree‐based pipeline optimization tool

AbstractOvarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.

Cancer cell‐specific protein delivery by optoporation with laser‐irradiated gold nanorods

ABSTRACTThe delivery of macromolecules into living cells is challenging since in most cases molecules are endocytosed and remain in the endo‐lysosomal pathway where they are degraded before reaching their target. Here, a method is presented to selectively improve cell membrane permeability by nanosecond laser irradiation of gold nanorods (GNRs) with visible or near‐infrared irradiation in order to deliver proteins across the plasma membrane, avoiding the endo lysosomal pathway. GNRs were labeled with the anti‐EGFR (epidermal growth factor receptor) antibody Erbitux to target human ovarian carcinoma cells OVCAR‐3. Irradiation with nanosecond laser pulses at wavelengths of 532 nm or 730 nm is used for transient permeabilization of the cell membranes. As a result of the irradiation, the uptake of an anti‐Ki‐67 antibody was observed in about 50 % of the cells. The results of fluorescence lifetime imaging show that the GNR detached from the membrane after irradiation.

Multi‐Parameter Deep Learning Combined With Fluorescence Lifetime Imaging Microscopy for Non‐Invasive and Label‐Free Endometrial Cancer Screening

ABSTRACT This study presents a noninvasive and label‐free endometrial cancer screening approach combining fluorescence lifetime imaging microscopy (FLIM) with a multi‐parameter deep learning algorithm. The autofluorescence signals of NAD(P)H in cervical exfoliated cells of participants ( n  = 71) were detected by FLIM. Two key metabolic parameters—mean fluorescence lifetime ( t m ) and protein‐bound fraction ( a 2 )—were extracted using FLIM data, which reflect malignancy‐associated biochemical changes. Single‐parameter models based on individual parameters ( t m or a 2 ) and a multi‐parameter model integrating both parameters were constructed. The results showed that single‐parameter models suffered from sensitivity‐specificity imbalance while the multi‐parameter model leveraging feature complementarity significantly improved predictive performance—achieving 100% sensitivity and 92% specificity in external testing, with an AUC of 0.92, representing an improvement of 0.17–0.31 over single‐parameter models. These findings suggest that combining multi‐parameter deep learning strategies with FLIM holds strong potential for risk prediction of endometrial cancer, offering a new approach for noninvasive clinical screening.