Giuseppe Argenziano. A short summary of this paper. In advanced Zito Marino, F. Fine needle aspiration Grimaldi, A. Predictive cytological samples may be inconvenient, as it is a time and biomaterial-consuming technique. Diagnostics , 11, melanoma. BRAF molecular analysis was performed on the same cytological samples or on the Molecular analysis was considered the gold standard. Hyperpigmentation represents the main limitation of the technique.
Cutaneous melanoma CM is the most aggressive skin neoplasm, responsible for This article is an open access article approximately 61, deaths per year worldwide [1]. Beyond cases creativecommons. Metastatic CM incidence is less than 5 cases per , per year [5]. The diagnosis of CM metastases is often based on clinical and instrumental find- ings and a direct sampling is usually unnecessary. Moreover, deep visceral CM metastases may be difficult to sample by percutaneous techniques such as FNA.
However, some reports have shown that the use of FNA with image guidance demonstrates excellent diagnostic performance for the diagnosis of CM metastases, being that the sensitivity SE and specificity SP range is between A correct sampling of the lesion and adequate cellularity is mandatory to optimize the diagnostic performance of FNC. Particularly, false-negative cases could be related to inadequate sampling. In addition, above all in the cases of a suspicious metastatic site of difficult access, the low cellularity of the samples containing only scattered malignant cells does not permit additional ancillary methods for better characterization [3].
However, when a diagnosis of metastatic CM is posed, the current therapeutic approach is based on advanced medical treatment. Indeed, historically, surgery was the only therapy for CM, and therapeutic options were not available for patients with advanced disease. In the last few years, a better understanding of the molecular landscape of CM led to the development of new therapies for advanced disease.
The identification of BRAF mutations has a predictive value in advanced CM, leading to the selection of patients who may benefit, in the presence of a V mutation, from treatment with MAPK inhibitors [9].
This evaluation can be carried out using different molecular procedures, including direct sequencing of the PCR product, pyrosequencing, RealTime PCR, molecular hybridization on filter and mass spectrometry [10]. Recently, a BRAF VE mutation- specific immunohistochemical antibody was introduced, with sensitivity and specificity comparable to the molecular tests in histological samples [11—13].
It was consequently pro- posed, and applied, as a screening tool for a rapid and cheaper assessment of BRAF VE mutational status in histological samples [11—14]. In the clinical setting of the advanced disease, the biomaterial obtained from metastasis through FNA procedure for diagnosis could be also used for the assessment of the current BRAF status of the patients, who cannot benefit from surgery with radical intent, avoiding unnecessary surgical stress and public health charges [15—17].
Indeed, although the BRAF status usually does not change in the metastasis with respect to the primitive CM, a recent metanalysis underlined the discrepancy between the primitive CM and relative metastasis, calculating a change from BRAF mutation in primitive CM to wild type in relative metastasis in In addition, metastasis could be the only clinical feature of melanoma with an unknown BRAF status.
Thus, cytological samples could represent the only biomaterials useful for detecting BRAF status [16,17]. Finally, a different BRAF status of the metastasis with respect to known primitive tumors could also be the expression of another tumor with a different mutational status [19]. For all these reasons, the recommendation of the current guidelines is to determine the mutational status of the metastasis, when possible [20]. However, some limitations about the use of cytological samples for predictive pur- poses should be addressed.
First, the largest limitation to obtain reliable results is the number of neoplastic cells in the cytological sample, sometimes unsatisfactory also for diagnostic purposes [18].
Thus, in such cases, it is required to resort to a surgical sampling of the metastasis for the BRAF status assessment, with further stress for the patient, or to the primitive tumor [21,22]. Indeed, the immunohistochemical evaluation requires a relatively smaller amount of neoplastic cells, if compared to molecular extractive methods for mutation detections [11—14]. Thus, in the primitive CM, when very thin or previously highly consumed for diagnosis, BRAF immunohistochemical detection could offer higher diagnostic accuracy than molecular testing [11—14].
A further limitation is related to the complete consumption of the direct smears of the metastasis cytological sample for DNA extraction, with loss of archival biological material. Finally, molecular analysis is expensive, requires experienced technicians and is not widespread in all labo- ratories worldwide.
Thus, although the application of molecular analysis to cytological samples is generally successful, the use of immunocytochemistry ICC for predictive tests in advanced CM on cytological samples could present some advantages. Materials and Methods 2. Negative cases were excluded.
Positive cases were selected in our series according to the following criteria: 1 diagnosis of CM metastases rendered on FNA samples; 2 the realization of a cell-block CB ; 3 the presence of residual biomaterial in the CB; 4 molecular evaluation of BRAF mutational status performed on the same cytological sample or the corresponding histological sample, when surgery was performed.
Fifty-seven positive consecutive cases were initially retrieved. All the cases were reviewed by two expert cytopathologists MM, IC to confirm the diagnosis and to assess the presence of sufficient residual tumor cells in the CB.
The diagnosis was confirmed in all cases. Two cases were excluded because they did not include a CB, and 4 cases were excluded because the CB did not include residual biomaterial. One case was excluded because a molecular evaluation was not possible, as this case only included few neoplastic cells. Therefore, the final number of cases included in the series was Clinical and molecular data were collected from the archives of the Pathology Unit.
BRAF Immunocytochemistry Evaluation All immunostained slides were evaluated by two cytopathologists in absence of any information about molecular data. Immunostaining was primarily interpreted as positive or negative. We defined a case as positive if it showed diffuse cytoplasmic staining, according to data reported in histological series [13,23].
We considered a case as negative if no staining or only nuclear dot staining was present. Furthermore, the percentage of positive neoplastic cells and intensity of the staining were recorded. The percentage of positive neoplastic cells were calculated by comparing the stained neoplastic cells to the total number of neoplastic cells in the slide. Diagnostics , 11, 4 of 13 2. BRAF Molecular Analysis According to strategies followed during clinical management, molecular analyses were previously performed preferentially on cytological samples; when the number of neoplastic cells was not enough for extractive-based molecular testing, the analysis was conducted on the histological sample of the CM metastasis when the patients were submitted to surgery or on the primitive tumors if tissue from metastases were not available.
Torrent Suite Software v. Data were analyzed using Ion Reporter software [22] and further filtered through quality checking. Ethical Consideration The present study was retrospectively conducted on archival biological samples. Both the cytological and histological diagnoses, as well as molecular analysis of BRAF mutational status, had already been rendered in all cases.
At the time of the FNA procedure, a written consent, including the consent to use the diagnostic data for scientific purposes, had been obtained from each patient. The approval by the institutional ethical board was collected. Results 3. In detail, the location of CM metastases was: right axillary LNs in 14 cases, left axillary LNs in 8 cases, right inguinal LNs in 8 cases, left inguinal LNs in 4 cases, right cervical LNs in 5 cases, left intraparotid LNs in 2 cases, right intraparotid LN in 1 case, deep para-aortic LN in 1 case, right lung in 2 cases, left lung in 1 case, subcutis in 4 cases.
Clinical findings are summarized in Table 1. Clinical findings. Total Cases Table 1. Molecular Molecularevaluation evaluationofofthe the BRAF status was performed in all cases, particularly on cytological samples in 17 cases BRAF status was performed in all cases, particularly on cytological samples in 17 cases and on histological samples in 33 cases primitive or metastasis.
The staining intensity positivity of The staining intensity resultedheterogeneous, resulted heterogeneous,ranging rangingfrom fromslight slighttotostrong strongintensity intensity Figure Figure3. Percentage of of BRAFimmunocytochemistry-positive immunocytochemistry-positive cellsin in the15 15 positivecases. Notice the H operator for moving the hand from the mouse to the keyboard.
That operator may not be necessary if the user uses the hand already on the keyboard which pressed Shift to reach over and press Del. The second method clicks at the start of the word, then presses Del enough times to delete all the characters in the word.
The developers of the KLM model tested it by comparing its predications against the actual performance of users on 11 different interfaces 3 text editors, 3 graphical editors, and 5 command-line interfaces like FTP and chat.
The tasks were diverse but simple: e. Users were told the precise method to use for each task, and given a chance to practice the method before doing the timed tasks.
Each task was done 10 times, and the observed times are means of those tasks over all users. One flaw in this study is the way they estimated the time for mental operators — it was estimated from the study data itself, rather than from separate, prior observations.
Keystroke level models can be useful for comparing efficiency of different user interface designs, or of different methods using the same design. One kind of comparison enabled by the model is parametric analysis — e. Using the approximations in our keystroke level model, the shift-click method is roughly constant, while the Del-n-times method is linear in n.
So there will be some point n below which the Del key is the faster method, and above which Shift-click is the faster method. Predictive evaluation not only tells us that this point exists, but also gives us an estimate for n. But here the limitations of our approximate models become evident. So the approximation may be fine in this case.
KLM also assumes that all actions are serialized, even actions that involve different hands like moving the mouse and pressing down the Shift key. Planning, problem solving, different levels of working memory load can all affect time and error rate; KLM lumps them into the M operator. GOMS is a richer model that considers the planning and problem solving steps. Starting with the low-level O perators and M ethods provided by KLM, GOMS adds on a hierarchy of high-level G oals and subgoals like we looked at for task analysis and S election rules that determine how the user decides which method will be used to satisfy a goal.
Notice the selection rule that chooses between two methods for achieving the goal, based on an observation of how many characters need to be deleted. GOMS has several variants.
The benefit of the formal language is that each statement roughly corresponds to a primitive mental chunk, so you can estimate the learning time of a task by simply counting the number of statements in the model for the task.
The language also has statements that represent working memory operations Retain and Recall , so that excessive use of working memory can be estimated by executing the model. It tackles the serial assumption of KLM, allowing multiple operators to run at the same time.
We have a perceptual processor PP , a cognitive processor CP , and multiple motor processors MP , one for each major muscle system that can act independently. For GUI interfaces, the muscles we mainly care about are the two hands and the eyes. However, ANOVA and regression analysis give a dependent variable that is a numerical variable, while hierarchical optimal discriminant analysis gives a dependent variable that is a class variable.
Classification and regression trees CART are a non-parametric decision tree learning technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively. Decision trees are formed by a collection of rules based on variables in the modeling data set: Rules based on variables' values are selected to get the best split to differentiate observations based on the dependent variable Once a rule is selected and splits a node into two, the same process is applied to each "child" node i.
Alternatively, the data are split as much as possible and then the tree is later pruned. Each branch of the tree ends in a terminal node. Each observation falls into one and exactly one terminal node, and each terminal node is uniquely defined by a set of rules. A very popular method for predictive analytics is Leo Breiman's Random forests. Multivariate adaptive regression splines[edit] Multivariate adaptive regression splines MARS is a non-parametric technique that builds flexible models by fitting piecewise linear regressions.
An important concept associated with regression splines is that of a knot. Knot is where one local regression model gives way to another and thus is the point of intersection between two splines. In multivariate and adaptive regression splines, basis functions are the tool used for generalizing the search for knots. Basis functions are a set of functions used to represent the information contained in one or more variables.
Multivariate and Adaptive Regression Splines model almost always creates the basis functions in pairs. Multivariate and adaptive regression spline approach deliberately overfits the model and then prunes to get to the optimal model.
The algorithm is computationally very intensive and in practice we are required to specify an upper limit on the number of basis functions. Machine learning techniques[edit] Machine learning, a branch of artificial intelligence, was originally employed to develop techniques to enable computers to learn. Today, since it includes a number of advanced statistical methods for regression and classification, it finds application in a wide variety of fields including medical diagnostics, credit card fraud detection, face and speech recognition and analysis of the stock market.
In certain applications it is sufficient to directly predict the dependent variable without focusing on the underlying relationships between variables. In other cases, the underlying relationships can be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events. A brief discussion of some of these methods used commonly for predictive analytics is provided below.
A detailed study of machine learning can be found in Mitchell Neural networks[edit] Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. Neural networks are used when the exact nature of the relationship between inputs and output is not known.
A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training in neural networks used by different networks, supervised and unsupervised training, reinforcement learning, with supervised being the most common one. Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc.
Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc. Multilayer perceptron MLP [edit] The multilayer perceptron MLP consists of an input and an output layer with one or more hidden layers of nonlinearly-activating nodes or sigmoid nodes.
This is determined by the weight vector and it is necessary to adjust the weights of the network. The backpropagation employs gradient fall to minimize the squared error between the network output values and desired values for those outputs. The weights adjusted by an iterative process of repetitive present of attributes.
Small changes in the weight to get the desired values are done by the process called training the net and is done by the training set learning rule. Radial basis functions[edit] A radial basis function RBF is a function which has built into it a distance criterion with respect to a center. Such functions can be used very efficiently for interpolation and for smoothing of data. Radial basis functions have been applied in the area of neural networks where they are used as a replacement for the sigmoidal transfer function.
Such networks have 3 layers, the input layer, the hidden layer with the RBF non-linearity and a linear output layer. The most popular choice for the non-linearity is the Gaussian.
RBF networks have the advantage of not being locked into local minima as do the feed-forward networks such as the multilayer perceptron. Support vector machines[edit] support vector machines SVM are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data. They are learning machines that are used to perform binary classifications and regression estimations.
They commonly use kernel based methods to apply linear classification techniques to non-linear classification problems. There are a number of types of SVM such as linear, polynomial, sigmoid etc.
It is best employed when faced with the problem of 'curse of dimensionality' i. The method does not impose a priori any assumptions about the distribution from which the modeling sample is drawn.
It involves a training set with both positive and negative values. The sign of that point will determine the classification of the sample. In the k-nearest neighbour classifier, the k nearest points are considered and the sign of the majority is used to classify the sample.
The performance of the kNN algorithm is influenced by three main factors: 1 the distance measure used to locate the nearest neighbours; 2 the decision rule used to derive a classification from the k-nearest neighbours; and 3 the number of neighbours used to classify the new sample. It can be proved that, unlike other methods, this method is universally asymptotically convergent, i.
See Devroy et al. Geospatial predictive modeling[edit] Conceptually, geospatial predictive modeling is rooted in the principle that the occurrences of events being modeled are limited in distribution.
Occurrences of events are neither uniform nor random in distribution—there are spatial environment factors infrastructure, sociocultural, topographic, etc.
Geospatial predictive modeling attempts to describe those constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors that represent those constraints and influences.
Geospatial predictive modeling is a process for analyzing events through a geographic filter in order to make statements of likelihood for event occurrence or emergence. Tools[edit] Historically, using predictive analytics tools—as well as understanding the results they delivered— required advanced skills. However, modern predictive analytics tools are no longer restricted to IT specialists[citation needed].
As more organizations adopt predictive analytics into decision-making processes and integrate it into their operations, they are creating a shift in the market toward business users as the primary consumers of the information. Business users want tools they can use on their own.
These range from those that need very little user sophistication to those that are designed for the expert practitioner. The difference between these tools is often in the level of customization and heavy data lifting allowed. Such an XML-based language provides a way for the different tools to define predictive models and to share these between PMML compliant applications.
PMML 4. Criticism[edit] There are plenty of skeptics when it comes to computers and algorithms abilities to predict the future, including Gary King, a professor from Harvard University and the director of the Institute for Quantitative Social Science. Trying to understand what people will do next assumes that all the influential variables can be known and measured accurately.
Everything from the weather to their relationship with their mother can change the way people think and act. All of those variables are unpredictable. How they will impact a person is even less predictable. If put in the exact same situation tomorrow, they may make a completely different decision. This means that a statistical prediction is only valid in sterile laboratory conditions, which suddenly isn't as useful as it seemed before. Please help to improve this article by introducing more precise citations.
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