A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients

Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review

Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.

From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images

Survival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora of Artificial Intelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods have demonstrated remarkable proficiency in analyzing and interpreting whole slide images, yielding results comparable to those of expert pathologists. The complexity of AI-driven techniques is magnified by the distinctive characteristics of digital histology images, including their gigapixel size and diverse tissue appearances. Consequently, advanced patch-based methods are employed to effectively extract features that correlate with patient survival. These computational methods significantly enhance survival prediction accuracy and augment prognostic capabilities in cancer patients. The review discusses the methodologies employed in the literature, their performance metrics, ongoing challenges, and potential solutions for future advancements. This paper explains survival analysis and feature extraction methods for analyzing cancer patients. It also compiles essential acronyms related to cancer precision medicine. Furthermore, it is noteworthy that this is the inaugural review paper in the field. The target audience for this interdisciplinary review comprises AI practitioners, medical statisticians, and progressive oncologists who are enthusiastic about translating AI-driven solutions into clinical practice. We expect this comprehensive review article to guide future research directions in the field of cancer research.

Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature

ABSTRACT

Pathology serves as a promising field to integrate artificial intelligence into clinical practice as a powerful screening tool. Melanoma is a common skin cancer with high mortality and morbidity, requiring timely and accurate histopathologic diagnosis. This review explores applications of artificial intelligence in melanoma dermatopathology, including differential diagnostics, prognosis prediction, and personalized medicine decision-making.

Self-supervised artificial intelligence predicts recurrence, metastasis and disease specific death from primary cutaneous squamous cell carcinoma at diagnosis

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome (PO) including recurrence, metastasis and disease specific death (DSD) at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. In this multi-institutional study, we developed a state-of-the-art self-supervised deep-learning approach with interpretability power and demonstrated its ability to predict poor outcomes of cSCCs at the time of initial biopsy. By highlighting histomorphological phenotypes, our approach demonstrates that poor differentiation and deep invasion correlate with poor prognosis. Our approach is particularly efficient at defining poor outcome risk in Brigham and Women's Hospital (BWH) T2a and American Joint Committee on Cancer (AJCC) T2 cSCCs. This bridges a significant gap in our ability to assess risk among T2a/T2 cSCCs and may be useful in defining patients at highest risk of poor outcome at the time of diagnosis. Early identification of highest-risk patients could signal implementation of more stringent surveillance, rigorous diagnostic work up and identify patients who might best respond to early postoperative adjunctive treatment.

Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence

Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30-55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem.

Exploring the efficacy of artificial neural networks in predicting lung cancer recurrence: a retrospective study based on patient records

Background

Lung cancer remains a significant public health concern, accounting for a considerable number of cancer-related deaths worldwide. Neural networks have emerged as a promising tool that can aid in the diagnosis and treatment of various cancers. Consequently, there has been a growing interest in exploring the potential of artificial intelligence (AI) methods in medicine. The present study aimed to evaluate the effectiveness of a neural network in predicting lung cancer recurrence.

Methods

The study employed retrospective data from 2,296 medical records of patients diagnosed with lung cancer and admitted to the Warmińsko-Mazurskie Center for Lung Diseases in Olsztyn, Poland. The statistical software STATISTICA 7.1, equipped with the Neural Networks module (StatSoft Inc., Tulsa, USA), was utilized to analyze the data. The neural network model was trained using patient information regarding gender, treatment, smoking status, family history, and symptoms of cancer.

Results

The study employed a multilayer perceptron neural network with a two-phase learning process. The network demonstrated high predictive ability, as indicated by the percentage of correct classifications, which amounted to 87.5%, 89.1%, and 89.9% for the training, validation, and test sets, respectively.

Conclusions

The findings of this study support the potential usefulness of a neural network-based predictive model in assessing the risk of lung cancer recurrence. Further research is warranted to validate these findings and to explore AI's broader implications in cancer diagnosis and treatment.

Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images

The high incidence rates of basal cell carcinoma (BCC) cause a significant burden at pathology laboratories. The standard diagnostic process is time-consuming and prone to inter-pathologist variability. Despite the application of deep learning approaches in grading of other cancer types, there is limited literature on the application of vision transformers to BCC on whole slide images (WSIs). A total of 1832 WSIs from 479 BCCs, divided into training and validation (1435 WSIs from 369 BCCs) and testing (397 WSIs from 110 BCCs) sets, were weakly annotated into four aggressivity subtypes. We used a combination of a graph neural network and vision transformer to (1) detect the presence of tumor (two classes), (2) classify the tumor into low and high-risk subtypes (three classes), and (3) classify four aggressivity subtypes (five classes). Using an ensemble model comprised of the models from cross-validation, accuracies of 93.5%, 86.4%, and 72% were achieved on two, three, and five class classifications, respectively. These results show high accuracy in both tumor detection and grading of BCCs. The use of automated WSI analysis could increase workflow efficiency.

Machine learning to predict overall short-term mortality in cutaneous melanoma

BACKGROUND

Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.

METHODS

CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.

RESULTS

The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.

CONCLUSIONS

Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( unipd.link/melanomaprediction ). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model's accuracy beyond the original research context.

A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients.

A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images

Breast cancer is one of the deadliest diseases worldwide among women. Early diagnosis and proper treatment can save many lives. Breast image analysis is a popular method for detecting breast cancer. Computer-aided diagnosis of breast images helps radiologists do the task more efficiently and appropriately. Histopathological image analysis is an important diagnostic method for breast cancer, which is basically microscopic imaging of breast tissue. In this work, we developed a deep learning-based method to classify breast cancer using histopathological images. We propose a patch-classification model to classify the image patches, where we divide the images into patches and pre-process these patches with stain normalization, regularization, and augmentation methods. We use machine-learning-based classifiers and ensembling methods to classify the image patches into four categories: normal, benign, in situ, and invasive. Next, we use the patch information from this model to classify the images into two classes (cancerous and non-cancerous) and four other classes (normal, benign, in situ, and invasive). We introduce a model to utilize the 2-class classification probabilities and classify the images into a 4-class classification. The proposed method yields promising results and achieves a classification accuracy of 97.50% for 4-class image classification and 98.6% for 2-class image classification on the ICIAR BACH dataset.