PubMed · 4761194
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E Saxén, E Nikkilä. 1973. [Enlarged lymph node].. https://pubmed.ncbi.nlm.nih.gov/4761194/
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PURPOSE: To review the value of extended diagnostic work-up procedures and to compare the results of comprehensive or volume-restricted radiotherapy in patients presenting with cervical lymph node metastases from clinically undetectable squamous cell carcinoma. METHODS AND MATERIALS: A systematic review was undertaken of published papers up to May 2000. RESULTS: Positron emission tomography (PET) has an overall staging accuracy of 69%, with a positive predictive value of 56% and negative predictive value of 86%. With negative routine clinical examination and computerized tomography (CT) or magnetic resonance imaging (MRI), PET detected primary tumors in 5-25% of patients, whereas ipsilateral tonsillectomy discovered carcinoma in about 25% of patients. Laser-induced fluorescence imaging with panendoscopy and directed biopsies showed some encouraging preliminary results and warrants further study. All together, the reported mucosal carcinoma emergence rates were 2-13% (median, 9.5%) after comprehensive radiotherapy and 5-44% (median, 8%) after unilateral neck irradiation. The corresponding nodal relapse rates were 8-45% (median, 19%) and 31-63% (median, 51.5%), and 5-year survival rates were 34-63% (median, 50%) and 22-41% (median, 36.5%), respectively. Retrospective single-institution comparisons between comprehensive and unilateral neck radiotherapy did not show apparent differences in outcome. Prognostic determinants for survival are the N stage, number of nodes, extracapsular extension, and histologic grade. No data were found to support the benefit of chemotherapy in this disease. CONCLUSION: Physical examination, CT or MRI, and panendoscopy with biopsies remain the standard work-up for these patients. Routine use of PET or laser-induced fluorescence imaging cannot be firmly advocated based on presently available data. Although combination of nodal dissection with comprehensive radiotherapy yielded most favorable results, its impact on the quality of life should be recognized, and the confounding effects of patient selection for various treatment modalities on therapeutic outcome cannot be ruled out. A randomized trial comparing the therapeutic value of comprehensive vs. volume-limited radiotherapy is being considered.
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BACKGROUND: Considerable evidence has shown that the use of computational algorithms to combine pretreatment clinical and pathology results can enhance predictions of patient outcome. The aim of this study was to prove that the application of such methods to predict patient-specific likelihoods of organ-confined (OC) prostate carcinoma (PCA) may be helpful to patients and physicians when they are choosing an optimal treatment for carcinoma of the prostate. METHODS: The authors used clinical and quantitative pathology results from the biopsy specimens of 817 PCA patients who had been evaluated at a large national pathology reference laboratory. The pathology parameters that were measured included the number of positive cores, Gleason grades and score, percentage of tumor involvement, and the tumor location. The pathologic stage of these cases, as determined by results from radical prostatectomy, lymphadenectomy, or bone scan, categorized the PCA as either OC, non-OC due to capsular penetration only (NOC-CP) or advanced disease with metastasis (NOC-Mets), i.e., seminal vesicle and/or lymph-node positive or bone-scan positive. There were a total of 481 OC cases, 185 NOC-CP cases, and 151 NOC-Mets cases. Patient-specific prediction models were trained by ordinal logistic regression (OLOGIT) and genetically engineered neural networks (GENNs), and the resulting trained models were validated by biopsy information from an independent set of 116 PCA patients. RESULTS: When the authors applied a cutoff of >or= 35% for the n = 817 training set of OC, NOC-CP, and NOC-Mets predictive probabilities, the OLOGIT model predicted OC PCA with an accuracy of 91%, whereas the GENN model predicted the same with an accuracy of 95%. When the authors employed the n = 116 validation set (76 OCs, 31 NOC-CPs, and 9 NOC-Mets), the OLOGIT and GENN models correctly identified OC PCA with 91% and 97% accuracy, respectively. CONCLUSIONS: The value of combining patient pretreatment diagnostic pathology parameters to make predictions concerning the postoperative extent of pathology was illustrated clearly in this study. This finding further confirms the need to pursue such approaches for PCA disease management in the future, especially with the increasing prevalence of clinical T1c (American Joint Committee on Cancer, 1977) disease.